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Как запустить 4 независимые нейросети на одном GPU (16 ГБ) под FastAPI

2024–2025 годы стали переломными для индустрии машинного обучения. Нейросети окончательно вышли за пределы исследовательских лабораторий и стали частью повседневных продуктов: от генерации текста и изображений до обработки документов, речи и видео. Для разработчиков это означало одно — нейросети стали таким же инструментом, как базы данных или очереди сообщений, и всё чаще заказчики ожидают их наличия в своих проектах.

В этот момент многие команды оказались на развилке. С одной стороны — облачные API: OpenAI (ChatGPT), Anthropic (Claude), DeepSeek и десятки похожих сервисов. Они позволяют подключить мощную модель буквально за несколько минут и сразу получить результат, который ещё пару лет назад казался недостижимым. С другой стороны — локальные модели, которые можно развернуть на собственных серверах и полностью контролировать процесс обработки данных.

Почасовая аренда серверов с GPU-картами

Серверы с видеокартами NVIDIA — от доступных моделей начального уровня до профессиональных Tesla, в дата-центрах в России и Европе.

При длительной аренде — скидка до 35 %.

Посмотреть конфигурации →

У обоих подходов есть свои сильные и слабые стороны.

Облачные нейросети

Плюсы:

  • быстрая интеграция в любой проект;

  • минимум инфраструктурных забот;

  • стабильно высокое качество моделей.

Минусы:

  • данные компании отправляются на сторонние серверы (что критично для корпоративного сектора, финтеха и госструктур);

  • высокая стоимость при масштабировании — многие проекты с активным использованием API столкнулись с тем, что счета за inference растут быстрее бизнеса.

Локальные нейросети

Плюсы:

  • данные остаются внутри собственного контура;

  • предсказуемая экономика — вы платите только за железо и электричество;

  • полный контроль над моделями и пайплайнами.

Минусы:

  • необходимость инфраструктуры;

  • порог входа выше, чем у облачных решений.

Из названия статьи уже понятно, что дальше мы будем говорить именно о локальных нейросетях. Причём не в теории, а на практике — с реальным кодом и реальным сервером.

Основных возражений против локального подхода обычно два: «это дорого» и «это сложно поднимать». В рамках этой статьи я постараюсь оба тезиса аккуратно разобрать.

Во-первых, инфраструктура. Сегодня я покажу, как развернуть полноценный мультимодальный ML-сервис из четырёх нейросетей на видеокарте с 16 ГБ видеопамяти. Аренда такого сервера обойдётся примерно в 12 000 рублей в месяц, что уже сравнимо с затратами на облачные API при умеренной нагрузке.

Во-вторых, сложность. Если вы Python-разработчик и уже сталкивались с ML-инструментами, то знаете: запуск даже моделей уровня GPT-4-класса сегодня часто сводится к одной-двум командам благодаря таким инструментам, как vLLM или Ollama.

Метод, который мы будем использовать в этой статье, чуть сложнее «одной кнопки», но зато даёт больше гибкости и контроля. Следуя шаг за шагом, вы сможете запускать практически любые современные модели на относительно доступном железе и собирать из них рабочие продакшн-сервисы.

Что собираем в итоге

Теперь — к сути статьи. На большом практическом примере я хочу показать, что при работе с локальными нейросетями возможна полноценная кастомизация и сборка сложных ML-пайплайнов без вмешательства во внутреннее устройство моделей. Нам не понадобится дообучение, fine-tuning или «копание в мозге» нейронок — достаточно правильно собрать их в единый сервис.

Параллельно я хочу продемонстрировать ещё одну важную мысль: 16 ГБ видеопамяти сегодня — это не «ни о чём», а вполне рабочий минимум, на котором можно запустить полноценное мультимодальное приложение без обращения к облачным API.

В рамках статьи мы шаг за шагом сделаем следующее:

  1. Арендуем VPS‑сервер с выделенной GPU.

  2. Купим доменное имя и позже привяжем его к сервису.

  3. Выполним базовую настройку сервера под ML‑задачи.

  4. Напишем FastAPI‑приложение, которое будет выступать оболочкой над локальными нейросетями, загруженными через PyTorch и Hugging Face Transformers.

  5. Запустим сервис в режиме постоянной работы с помощью systemd.

  6. Опубликуем приложение наружу и настроим доступ по доменному имени.

Инфраструктура

GPU-сервер мы будем арендовать у провайдера Hostkey. Причина выбора простая: на момент написания статьи у них можно найти один из самых доступных вариантов с GPU такого уровня.

Используемая конфигурация:

  • VPS

  • 8 vCPU

  • 32 ГБ RAM

  • 240 ГБ SSD

  • GPU: RTX A4000 (16 ГБ VRAM)

Стоимость — около 12 000 рублей в месяц (актуальная цена, которая была на конец 2025 года), что делает такой сетап разумной альтернативой облачным ML-API при постоянной нагрузке.

Конфигурация на которой будет развернут проект.
Конфигурация на которой будет развернут проект.

Какие нейросети будем поднимать

В качестве примера мы соберём мультимодальный пайплайн из четырёх моделей, каждая из которых решает свою конкретную задачу:

  • DeepSeek OCR — извлечение текста из изображений и PDF (компьютерное зрение);

  • Whisper Large v3 (RU) — распознавание русской речи из аудио с преобразованием в текст;

  • Qwen2.5-3B — LLM‑модель‑«говорун», которую будем использовать:

    • как чат‑модель;

    • как инструмент нормализации и постобработки текста, полученного от OCR и ASR;

  • MMS‑TTS (RU) — озвучивание русского текста.

В результате получится следующий логический пайплайн:

OCR → ASR → LLM → TTS

Такой подход позволяет принимать на вход изображения, PDF или аудио, приводить данные к аккуратному текстовому виду, при необходимости обрабатывать их через LLM и возвращать результат в виде озвученного ответа.

Основной технический стек

Вся реализация будет выполнена на Python. Глубоких знаний машинного обучения не потребуется, но базовое понимание Python и серверной разработки будет полезно.

Ключевые библиотеки:

  • FastAPI — HTTP‑оболочка сервиса;

  • torch — базовый фреймворк для работы с моделями;

  • transformers — загрузка и управление нейросетями из экосистемы Hugging Face.

Этот стек позволяет держать код компактным, управлять памятью и dtype моделей, а также без лишних зависимостей собрать продакшн-готовый ML-сервис.

В следующем разделе мы переходим от теории к практике и начнём с самого основания — настройки инфраструктуры и сервера под GPU.

Подготовка инфраструктуры

Прежде чем переходить к коду и нейросетям, нам нужно подготовить базовую инфраструктуру: арендовать сервер с GPU, купить доменное имя и настроить окружение для разработки.

Аренда VPS с GPU

Для примера будем использовать провайдера Hostkey, но общая логика подойдёт для любого хостинга с GPU.

Порядок действий следующий:

  1. Регистрируемся в Hostkey (если аккаунта ещё нет).

  2. Заходим в личный кабинет.

  3. Переходим в раздел «Новый сервер».

  4. Выбираем «Серверы с GPU».

  5. Подбираем сервер с минимум 16 ГБ видеопамяти.
    В качестве операционной системы рекомендую Ubuntu 24.04 — на ней выполнялись все тесты проекта.

  6. Арендуем сервер. У Hostkey доступна почасовая оплата, что удобно для тестов и экспериментов.

Заказ Сервера в Инвапи Hostkey
rutube.ru

После оплаты на почту придёт письмо с данными для доступа:

  • IP-адрес сервера;

  • имя пользователя;

  • пароль.

Сразу копируем IP-адрес сервера. Он нам будет нужен на следующем этапе.
Сразу копируем IP-адрес сервера. Он нам будет нужен на следующем этапе.

Покупка доменного имени

Домен можно купить у любого регистратора — принципиальной разницы нет. Важно лишь одно: после покупки зайти в DNS-настройки и добавить A-запись, указывающую на IP-адрес вашего сервера.

Это понадобится позже, когда мы будем публиковать FastAPI-приложение наружу.

Показываю пример покупки через сервер reg.ru (регистратор доменных имен может быть любым, принципиальной разницы нет).

  • Регистрируемся на сайте регистратора

  • Заходим в меню покупки доменных имен. Первое, что нас интересует - это проверка того, что доменное имя свободно. Проверка доступности домена: https://www.reg.ru/buy/domains/

a8d73fa543a4d3015c31ce8359cf293e.png

  • Если имя свободно - можно переходить к покупке. Убираем все доп услуги. Нам нужен только домен!

На момент написания статьи работал промокод NY2026, который позволял в REG.RU купить домен за 1 рубль на год. Проверьте, может ещё работает.
На момент написания статьи работал промокод NY2026, который позволял в REG.RU купить домен за 1 рубль на год. Проверьте, может ещё работает.

  • Далее убедитесь, что в настройках домена указаны DNS-серверы ns1.reg.ru и ns2.reg.ru. Эти серверы REG.RU позволяют гибко привязать свой собственный IP-адрес сервера, без привязки к стороннему хостингу.

Для редактирования кликаем на "изменить"
Для редактирования кликаем на "изменить"

  • Далее добавляем А записи. Сделал отдельную A-запись для доступа к домену через www.

После создания А-записей ждем 15-30 минут и можно настраивать Nginx на сервере (займемся этим немного позже)
После создания А-записей ждем 15-30 минут и можно настраивать Nginx на сервере (займемся этим немного позже)

Первичная настройка сервера

Отложим пока купленный домен и перейдём к подготовке сервера для разработки. На этом этапе подключимся к серверу по SSH, обновим пакеты, установим необходимый софт, драйверы и полностью подготовим систему к запуску FastAPI-проекта с четырьмя локальными нейросетями под капотом.

Подключаемся к серверу по SSH:

ssh root@IP_ADDRESS

Вводим пароль из письма.

Обновляем систему и ставим базовые зависимости:

sudo apt update && sudo apt install -y \
    nginx \
    certbot \
    python3 \
    python3-pip \
    python3-venv \
    python3-certbot-nginx

Установка NVIDIA-драйверов

Устанавливаем рекомендованные драйверы:

sudo ubuntu-drivers autoinstall

После установки обязательно перезагружаем сервер:

sudo reboot

После перезагрузки снова подключаемся по SSH и проверяем, что GPU корректно определяется системой:

nvidia-smi
Если видеокарта отображается — можно двигаться дальше.
Если видеокарта отображается — можно двигаться дальше.

Подготовка проекта и виртуального окружения

Создадим директорию под будущий проект, например в /home:

cd /home
mkdir project_name
cd project_name
16cbf3945b599e0143fa533869dcabad.jpg

Создаём виртуальное окружение и активируем его для теста:

python3 -m venv .venv
source .venv/bin/activate
Если окружение активировалось корректно — база для разработки готова.
Если окружение активировалось корректно — база для разработки готова.

Удобный SSH-доступ без пароля

Прежде чем мы нырнём в код, настроим удобный вход на VPS без постоянного ввода пароля. Это сильно упростит жизнь на следующих этапах — особенно когда поделюсь лайфхаком написания кода прямо на сервере, если локального железа нет под рукой.

Linux / macOS

# Настройки (замените на свои)
ALIAS_NAME=myserver
HOST=1.222.33.44
PORT=22
USER=username

# Создаём папку для ключей и генерируем ключ
mkdir -p "$HOME/.ssh/project_keys/$ALIAS_NAME" && \
ssh-keygen -t ed25519 -f "$HOME/.ssh/project_keys/$ALIAS_NAME/id_ed25519"

# Копируем публичный ключ на сервер
ssh-copy-id -i "$HOME/.ssh/project_keys/$ALIAS_NAME/id_ed25519.pub" -p "$PORT" "$USER@$HOST"

# Добавляем алиас в ~/.ssh/config
KEY_PATH="$HOME/.ssh/project_keys/$ALIAS_NAME/id_ed25519"
cat >> "$HOME/.ssh/config" <<EOF
Host $ALIAS_NAME
    HostName $HOST
    Port $PORT
    User $USER
    IdentityFile $KEY_PATH
    RemoteCommand sudo -i
    RequestTTY yes
EOF

chmod 600 "$HOME/.ssh/config"

Windows (PowerShell)

$ALIAS_NAME = "myserver"
$HOST = "1.222.33.44"
$PORT = "22"
$USER = "username"

# Создаём папку и генерируем ключ
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.ssh\project_keys\$ALIAS_NAME" | Out-Null
ssh-keygen -t ed25519 -f "$env:USERPROFILE\.ssh\project_keys\$ALIAS_NAME\id_ed25519"

# Копируем ключ вручную
type "$env:USERPROFILE\.ssh\project_keys\$ALIAS_NAME\id_ed25519.pub" | 
ssh -p $PORT "$USER@$HOST" "mkdir -p ~/.ssh && cat >> ~/.ssh/authorized_keys"

# Добавляем алиас в config
$KEY_PATH = "$env:USERPROFILE\.ssh\project_keys\$ALIAS_NAME\id_ed25519"
@"
Host $ALIAS_NAME
    HostName $HOST
    Port $PORT
    User $USER
    IdentityFile $KEY_PATH
    RemoteCommand sudo -i
    RequestTTY yes
"@ | Add-Content "$env:USERPROFILE\.ssh\config"

Готово! Теперь подключайтесь одной командой:

ssh myserver

Инфраструктура готова. Дальше — подключение VS Code к серверу, открытие папки проекта и самое вкусное — разработка FastAPI-оболочки с четырьмя локальными нейросетями.

Как вести разработку без мощного локального железа

Даже если в статье мы используем GPU с 16 ГБ видеопамяти, на практике далеко не у каждого разработчика есть такое железо под рукой. А что говорить о более тяжёлых конфигурациях?

Вести разработку напрямую на сервере через SSH — неудобно. Но есть простой и очень эффективный лайфхак.

VS Code + Remote SSH

Решение — расширение Remote SSH для VS Code. Оно позволяет работать с кодом на удалённом сервере так, будто проект находится локально.

Как это работает

  • VS Code устанавливает на сервер небольшой server‑агент.

  • Все операции (терминал, git, запуск скриптов, линтеры) выполняются на VPS.

  • В локальном VS Code вы просто редактируете файлы и управляете проектом.

Что нужно сделать

  1. Установить расширение Remote — SSH (ms-vscode-remote.remote-ssh).

  2. Убедиться, что SSH‑доступ к серверу без пароля работает:

    ssh myserver

  3. В VS Code нажать F1 → Remote-SSH: Connect to Host... → выбрать подготовленный ранее сервер (myserver).

После подключения выбрать папку проекта на сервере в которой уже установлено виртуальное окружение — она откроется как обычный workspace.
После подключения выбрать папку проекта на сервере в которой уже установлено виртуальное окружение — она откроется как обычный workspace.
Переходим в папку
Переходим в папку

Дисклеймер перед разработкой

Сразу небольшой дисклеймер. В проекте получилось достаточно много кода. Как это обычно бывает: начинаешь с «пары эндпоинтов», а дальше архитектура постепенно обрастает вспомогательными модулями, обработкой ошибок, управлением памятью и прочими необходимыми вещами.

В рамках статьи я не буду разбирать каждую строчку кода. Вместо этого мы сосредоточимся на ключевых и нетривиальных моментах:

  • архитектуре приложения;

  • загрузке и инициализации моделей;

  • управлении GPU-памятью;

  • интеграции PyTorch + Transformers с FastAPI.

Полный исходный код проекта выложен в открытом доступе GitHub: https://github.com/Yakvenalex/FastAPIAIModelsProject

Для удобства изучения дальнейшей информации - советую сразу клонировать проект себе и открыть его в IDE в котором вы привыкли работать:

git clone https://github.com/Yakvenalex/FastAPIAIModelsProject

Если по ходу чтения появятся вопросы или захочется обсудить реализацию с другими разработчиками, приглашаю в мой Telegram-канал «Лёгкий путь в Python». Там я публикую дополнительные материалы, исходники учебных проектов и разбираю практические кейсы. На момент написания статьи сообщество насчитывает более 5000 участников.

Приступаем к разработке

Предполагаю, что к этому моменту у вас:

  • уже арендован VPS с GPU;

  • установлен NVIDIA-драйвер;

  • настроен удобный доступ по SSH;

  • VS Code подключён к серверу через Remote SSH.

Теперь можно переходить к разработке самого сервиса.

Общая архитектура проекта

На высоком уровне архитектура выглядит следующим образом:

  • FastAPI выступает в роли HTTP‑оболочки;

  • каждая нейросеть инкапсулирована в отдельный модуль;

  • модели загружаются через PyTorch + Transformers;

  • отдельный менеджер управляет GPU‑памятью и жизненным циклом моделей.

Базовые файлы в корне проекта

В корне проекта создаём два важных файла:

.env

Файл с переменными окружения (пути к кешам, настройки памяти, параметры запуска). Мы будем использовать его для конфигурации сервиса без хардкода значений.

requirements.txt

Список зависимостей проекта:

# FastAPI и веб-сервер
fastapi==0.115.5
uvicorn[standard]==0.34.0
python-multipart==0.0.18

# PyTorch и Transformers
torch==2.5.1
torchvision==0.20.1
transformers==4.47.1
accelerate==1.2.1
bitsandbytes==0.45.0

# Работа с изображениями и PDF
Pillow==11.0.0
pdf2image==1.17.0
pypdf==5.1.0

# Работа с аудио
librosa==0.10.2.post1
soundfile==0.12.1
scipy==1.14.1

# Дополнительные утилиты
python-dotenv==1.0.1
pydantic==2.10.3
pydantic-settings==2.6.1
addict==2.4.0
einops==0.8.1
easydict==1.13
protobuf==5.29.2
sentencepiece==0.2.0

Большинство этих библиотек используются напрямую или косвенно самими моделями. Особенно это касается:

  • accelerate и bitsandbytes — для управления памятью и квантизацией;

  • sentencepiece и protobuf — для корректной загрузки токенизаторов;

  • библиотек для работы с аудио и изображениями, которые требуются OCR и ASR моделям.

Кеши моделей

В корне проекта автоматически будут созданы две директории:

  • models_cache — кеш загруженных моделей;

  • offload_cache — временное хранилище для offload‑операций.

Мы вернёмся к ним позже, когда будем говорить об оптимизации использования GPU-памяти.

Структура папки app

Основная логика приложения лежит в папке app:

app/
├── models/
├── routers/
├── utils.py
├── memory_manager.py
├── config.py
└── main.py

Кратко по назначению:

  • models/
    Файлы, отвечающие за загрузку и конфигурацию конкретных нейросетей.

  • routers/
    FastAPI‑роутеры.
    Один файл — один логический раздел API (OCR, ASR, LLM, TTS).

  • utils.py
    Вспомогательные функции, используемые в разных частях приложения.

  • memory_manager.py
    Менеджер памяти — ключевой компонент всей архитектуры.
    Он отвечает за:

    • загрузку и выгрузку моделей;

    • контроль использования GPU;

    • предотвращение OOM‑ошибок.
      Этому модулю мы посвятим отдельный раздел статьи.

  • config.py
    Централизованные конфигурации приложения.

  • main.py
    Точка входа. Здесь происходит сборка FastAPI‑приложения, подключение роутеров и инициализация сервисов.

Структура проекта может отличаться — это не догма. Важно лишь понять общий принцип связки FastAPI → PyTorch → Transformers и подход к организации кода.

На этом подготовительная часть завершена. Дальше мы начнём разбирать работу ключевых модулей приложения, начиная с архитектуры FastAPI и инициализации моделей.

Далее — как именно мы загружаем и держим несколько нейросетей в GPU-памяти, не убивая сервер.

Архитектура сервиса: как FastAPI управляет нейросетями

Прежде чем говорить об управлении памятью, важно понять как вообще устроен сервис и какую роль в нём играет FastAPI. Без этого дальнейшие оптимизации будут выглядеть как набор разрозненных костылей.

Общая концепция

В основе проекта лежит FastAPI — асинхронный веб-фреймворк, который в данном случае выступает не ML-инструментом, а оркестратором.

FastAPI:

  • принимает HTTP-запросы от клиентов;

  • маршрутизирует их к нужной нейросети;

  • управляет жизненным циклом моделей;

  • возвращает структурированный JSON-ответ.

Сами нейросети изолированы от веб-слоя и работают как независимые вычислительные компоненты.

Клиент (HTTP / JSON)
        │
        ▼
┌─────────────────────────────┐
│        FastAPI              │
│  ┌───────────────────────┐ │
│  │ Routers (/ocr /asr…)  │ │
│  └──────────┬────────────┘ │
│             ▼              │
│    Model Managers           │
│ (lazy load + caching)       │
└──────────┬───────────┬─────┘
           │           │
      ┌────▼───┐  ┌────▼────┐
      │ GPU    │  │ CPU RAM │
      │ 16 GB  │  │ 32 GB   │
      └────┬───┘  └────┬────┘
           │           │
      ┌────▼─────────────────┐
      │   AI Models           │
      │  DeepSeek / Whisper  │
      │  MMS / Qwen          │
      └──────────────────────┘

Модульная архитектура и разделение ответственности

Проект организован по принципам Clean Architecture — каждый слой отвечает только за свою зону ответственности.

app/
├── main.py            # Точка входа FastAPI
├── config.py          # Конфигурация
├── routers/           # HTTP API слой
│   ├── ocr.py
│   ├── asr.py
│   ├── tts.py
│   └── chat.py
├── models/            # Обертки над AI-моделями
│   ├── base.py
│   ├── deepseek_ocr.py
│   ├── whisper_asr.py
│   ├── mms_tts.py
│   └── qwen_chat.py
└── utils.py           # Утилиты и вспомогательная логика

Такое разделение позволяет:

  • менять модели без переписывания API;

  • тестировать слои независимо;

  • масштабировать проект без архитектурного рефакторинга.

Слой 1: Routers — HTTP API Gateway

Роутеры — это тонкий HTTP-слой, который не знает, как работает модель, а знает только что нужно сделать.

Задачи роутеров:

  • валидация входных данных;

  • получение модели через менеджер;

  • запуск инференса;

  • формирование ответа.

Пример OCR-эндпоинта:

@router.post("/extract-text")
async def extract_text(
    file: UploadFile = File(...),
    normalize: bool = False
):
    # Валидация
    if file.content_type not in allowed_types:
        raise HTTPException(400, "Неверный тип файла")

    model = get_ocr_model()

    # Инференс в отдельном потоке
    text = await asyncio.to_thread(model.predict, file_data)

    # Опциональная нормализация через LLM
    if normalize:
        text = await normalize_with_llm(text)

    return {"text": text, "status": "success"}

Ключевые особенности:

  • async def — FastAPI не блокирует event loop;

  • тяжёлые операции выполняются через asyncio.to_thread;

  • всегда возвращается предсказуемый JSON.

Слой 2: Model Managers — умные прокси к AI

Model Manager — это прослойка между роутером и моделью, отвечающая за её жизненный цикл.

Используется паттерн Singleton — каждая модель существует в одном экземпляре.

ocr_model: DeepSeekOCR = None

def get_ocr_model() -> DeepSeekOCR:
    global ocr_model
    if ocr_model is None:
        ocr_model = DeepSeekOCR(...)
    return ocr_model

Почему это важно:

  • загрузка модели занимает 5–20 секунд;

  • потребляет 5–10 GB памяти;

  • создавать новый экземпляр на каждый запрос — катастрофа.

Жизненный цикл модели

  1. Первый запрос к эндпоинту.

  2. Менеджер создаёт объект модели.

  3. Проверяет: загружена ли она в память.

  4. При необходимости — загружает с учётом лимитов.

  5. Обновляет last_used.

  6. Повторные запросы выполняются мгновенно.

  7. После простоя модель автоматически выгружается.

Слой 3: Model Wrappers — единый интерфейс для всех моделей

Все модели наследуются от базового класса:

class BaseModel(ABC):
    def is_loaded(self) -> bool: ...
    def load(self): ...
    def unload(self): ...
    def predict(self, *args, **kwargs): ...

Зачем это нужно:

  • унификация работы с Transformers, Whisper, TTS;

  • менеджер памяти работает с любой моделью;

  • добавление новой модели = новый класс + минимум кода.

Пример: DeepSeekOCR, WhisperASR, QwenChat — разные библиотеки, одинаковый интерфейс.

Полный поток данных: от запроса до ответа

На примере OCR:

  1. Клиент отправляет POST /ocr/extract-text.

  2. FastAPI маршрутизирует запрос.

  3. Происходит валидация входных данных.

  4. Получаем модель через get_ocr_model().

  5. При необходимости — ленивая загрузка.

  6. Инференс в отдельном потоке.

  7. Опциональная постобработка через LLM.

  8. Формируется JSON‑ответ.

FastAPI при этом остаётся:

  • быстрым;

  • неблокирующим;

  • предсказуемым.

Почему именно такая архитектура

Коротко по причинам:

  1. Singleton для моделей
    — минимальный overhead и переиспользование памяти.

  2. Чёткое разделение Router / Model
    — API не зависит от конкретной реализации модели.

  3. Базовый класс
    — единый контракт для менеджера памяти.

  4. Async‑friendly подход
    — FastAPI остаётся отзывчивым даже при тяжёлых инференсах.

Итог

Мы получили архитектуру, которая:

  • легко расширяется;

  • стабильно работает под нагрузкой;

  • готова к продакшену;

  • и, самое главное, позволяет управлять памятью осознанно.

В следующем разделе мы разберём как именно эта архитектура позволяет запускать 4 нейросети на 16 GB VRAM и почему без неё сервис будет падать.

Управление памятью: как запустить 4 AI-модели на 16 GB VRAM

Этот раздел — ключевой во всей статье. Я намеренно выбрал сложную конфигурацию: четыре достаточно тяжёлые нейросети и всего 16 GB видеопамяти. Это не демонстрация «в вакууме», а попытка показать реальную ситуацию, в которой оказываются большинство продакшн-проектов.

Если ничего не делать с памятью, такой сервис будет регулярно падать с CUDA out of memory, независимо от того, насколько аккуратно написан код.

Почему это критично

Модели в проекте действительно прожорливые:

  • Qwen 2.5-3B (LLM) — компактная чат‑модель, но всё равно ~4–5 GB VRAM;

  • DeepSeek OCR — визуальная модель, на пиках ~6–7 GB;

  • Whisper Large‑v3 — крупная ASR‑модель, ~5–6 GB;

  • MMS‑TTS — относительно лёгкая, но тоже ~2–3 GB.

Если попытаться держать их все одновременно в памяти, получится: 17–21 GB VRAM при наличии всего 16 GB

Без продуманной стратегии управления памятью приложение будет нестабильным. Поэтому дальше — не «оптимизации ради оптимизаций», а необходимые инженерные решения.

Стратегия управления памятью

В проекте используется многоуровневый подход, который можно свести к четырём ключевым идеям.

1. Очистка CUDA-контекста после каждого инференса

Проблема

После обработки запроса модель не освобождает всю память автоматически. В видеопамяти остаются:

  • промежуточные активации;

  • KV-cache;

  • временные тензоры.

Пример для OCR:

  • загрузка модели: ~6 GB VRAM;

  • после одного инференса: ~7 GB;

  • после нескольких запросов подряд: 8–9 GB.

Память накапливается как снежный ком.

Решение

Принудительно очищать CUDA-кеш после каждого инференса:

def predict(self, image_data: bytes) -> str:
    result = self.model.generate(...)

    # КРИТИЧНО: очистка контекста
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        gc.collect()

    return result

Эта простая операция:

  • экономит 1–3 GB VRAM;

  • предотвращает накопление «мусора»;

  • резко снижает вероятность OOM при серии запросов.

2. Гибридное распределение GPU / CPU памяти

Проблема

Даже с очисткой контекста бывают пики, когда GPU-памяти физически не хватает — особенно при параллельных запросах.

Решение

Использовать device_map="auto" + жёсткие лимиты памяти и CPU offload.

Пример конфигурации:

ocr_max_gpu_memory_gb = 10.0
whisper_max_gpu_memory_gb = 10.0
tts_max_gpu_memory_gb = 8.0
chat_max_gpu_memory_gb = 12.0

cpu_offload_memory_gb = 25.0

При загрузке модели:

max_memory = {
    0: f"{gpu_mem}GB",
    "cpu": f"{cpu_mem}GB"
}

model = AutoModel.from_pretrained(
    model_name,
    device_map="auto",
    max_memory=max_memory
)

Как это работает:

  • модель сначала пытается уместиться в GPU;

  • если не влезает — часть слоёв уходит в RAM;

  • GPU никогда не превышает заданный лимит.

Цена — небольшая потеря скорости. Выгода — полное отсутствие OOM-крэшей.

3. Lazy Loading + Auto-Unload (ленивая загрузка и выгрузка)

Ключевой вопрос

Зачем держать в памяти все модели сразу, если в конкретный момент используется только одна?

Lazy Loading

Модели не загружаются при старте приложения.

@router.post("/extract-text")
async def extract_text(file: UploadFile):
    model = get_ocr_model()

    if not model.is_loaded():
        logger.info("Загрузка OCR модели по требованию...")
        await asyncio.to_thread(model.load)

    return await asyncio.to_thread(model.predict, file)

Экономия:

  • при старте сервиса: ~0 GB VRAM;

  • память используется только при реальных запросах.

Auto-Unload

Если модель не используется некоторое время — она выгружается автоматически.

class BaseModel:
    def __init__(self):
        self.last_used: Optional[datetime] = None

    def update_last_used(self):
        self.last_used = datetime.now()

    def get_idle_time_minutes(self) -> float:
        if self.last_used is None:
            return 0.0
        delta = datetime.now() - self.last_used
        return delta.total_seconds() / 60.0

    def should_auto_unload(self, timeout_minutes: int) -> bool:
        return self.get_idle_time_minutes() > timeout_minutes

Фоновый менеджер:

class MemoryManager:
    async def _monitor_loop(self):
        while self._running:
            await asyncio.sleep(60)

            for name, model in self.all_models:
                if model.should_auto_unload(timeout_minutes=5):
                    logger.info(f"Выгрузка {name}")
                    model.unload()
                    torch.cuda.empty_cache()

Жизненный цикл модели:

  • запрос → загрузка → быстрые ответы;

  • 5 минут простоя → выгрузка → 0 GB VRAM;

  • новый запрос → повторная загрузка.

4. Защита от OOM: приоритетная выгрузка моделей

Проблема

Пользователь может почти одновременно вызвать OCR, ASR и Chat. Все модели начнут загружаться и переполнят GPU.

Решение

Перед загрузкой новой модели:

  1. проверяем свободную память;

  2. выгружаем самые «долго простаивающие» модели.

def auto_unload_old_models_if_needed(required_gb: float = 2.0):
    free_gb = get_free_gpu_memory()
    if free_gb >= required_gb:
        return

    loaded_models.sort(key=lambda x: x[2], reverse=True)

    for name, model, idle_time in loaded_models:
        if not model.is_loaded():
            continue

        model.unload()
        torch.cuda.empty_cache()

        if get_free_gpu_memory() >= required_gb:
            return

Использование в эндпоинтах:

if not model.is_loaded():
    await asyncio.to_thread(auto_unload_old_models_if_needed, required_gb=2.5)
    await asyncio.to_thread(model.load)

Итог: стабильная работа на 16 GB VRAM

Благодаря этой стратегии мы получаем:

  • Zero OOM crashes

  • в памяти только активные модели (обычно 1–2)

  • пиковое потребление: 10–12 GB вместо 21 GB

  • 4–6 GB запаса под пики

  • полностью автоматическое управление памятью

Типичное потребление VRAM

  • старт приложения: ~500 MB;

  • первый OCR‑запрос: ~6–7 GB;

  • OCR + Chat: ~10–11 GB;

  • 5 минут простоя: ~500 MB.

Именно это и позволяет стабильно запускать мультимодальные AI-сервисы на доступном железе, без серверов с 48+ GB VRAM.

Модуль app/models: унифицированные обёртки над AI-моделями

Перед тем как переходить к управлению памятью, нужно разобраться с ещё одним фундаментальным элементом проекта — модулем app/models. Именно здесь скрыта большая часть «магии», позволяющей управлять разными нейросетями одинаково.

Философия модуля: один интерфейс для разных библиотек

Главная боль при работе с несколькими AI-моделями — разрозненный API. Каждая библиотека живёт своей жизнью:

  • Transformers (Hugging Face) — AutoModel.from_pretrained(), model.generate();

  • Whisper — model.transcribe(), своя логика работы с аудио;

  • TTS — synthesizer.tts(), фонемы, sample rate;

  • PyTorch напрямую — forward(), ручное управление device.

Без абстракции код быстро превращается в набор if / elif:

# Плохо: каждая модель — отдельный мир
if model_type == "ocr":
    result = ocr_model.generate(...)
elif model_type == "asr":
    result = whisper_model.transcribe(...)
elif model_type == "tts":
    result = tts_model.tts_to_file(...)

Такой код:

  • сложно поддерживать;

  • невозможно масштабировать;

  • почти нереально оптимизировать по памяти.

Решение: единый интерфейс для всех моделей

В проекте используется простой, но очень мощный принцип:

Каждая модель — это «чёрный ящик» с тремя кнопками: load(), predict(), unload()

# Хорошо: все модели работают одинаково
for model in [ocr_model, asr_model, tts_model, chat_model]:
    if not model.is_loaded():
        model.load()

    result = model.predict(data)

    if model.get_idle_time_minutes() > 5:
        model.unload()

Что внутри модели — Transformers, Whisper, TTS или чистый PyTorch — остальному коду не важно.

Структура модуля app/models

app/models/
├── base.py              # Базовый абстрактный класс
├── deepseek_ocr.py      # OCR модель
├── whisper_asr.py       # ASR модель
├── mms_tts.py           # TTS модель
└── qwen_chat.py         # Chat / LLM модель

Каждый файл — это самодостаточная обёртка, которая:

  • знает, как загрузить модель;

  • умеет выполнять inference;

  • корректно управляет памятью;

  • безопасно выгружается из GPU / CPU.

BaseModel: контракт для всех моделей

В основе лежит абстрактный класс BaseModel. Он задаёт единый контракт, которому обязаны следовать все модели.

class BaseModel(ABC):
    def is_loaded(self) -> bool: ...
    def load(self): ...
    def unload(self): ...
    def predict(self, *args, **kwargs): ...

Что здесь важно:

  • @abstractmethod
    Python не позволит создать модель без реализации load(), unload() и predict().

  • Общее состояние
    self.model, self.last_used хранятся в базовом классе и работают одинаково для всех.

  • Мониторинг использования
    Методы get_idle_time_minutes() и should_auto_unload() используются менеджером памяти без знания конкретной модели.

Это делает весь остальной код типобезопасным и предсказуемым.

Централизованная конфигурация моделей

Все параметры вынесены в единый конфиг app/config.py на базе pydantic-settings.

class Settings(BaseSettings):
    device: str = "cuda"
    cache_dir: str = "./models_cache"

    # OCR
    deepseek_ocr_model: str = "deepseek-ai/DeepSeek-OCR"
    ocr_max_gpu_memory_gb: float = 10.0

    # ASR
    whisper_model: str = "antony66/whisper-large-v3-russian"
    whisper_max_gpu_memory_gb: float = 10.0

    # TTS
    mms_tts_model: str = "facebook/mms-tts-rus"
    tts_max_gpu_memory_gb: float = 8.0

    # Chat
    gemma_chat_model: str = "Qwen/Qwen2.5-3B-Instruct"
    chat_max_gpu_memory_gb: float = 12.0

Преимущества такого подхода:

  • все настройки в одном месте;

  • легко переопределять через .env;

  • типизация и валидация;

  • singleton — конфиг создаётся один раз.

Пример реализации: DeepSeek OCR

Рассмотрим, как абстракция выглядит на практике.

DeepSeekOCR наследуется от BaseModel и реализует только специфичную логику:

  • загрузку через Transformers;

  • обработку изображений и PDF;

  • OCR-инференс;

  • очистку памяти.

class DeepSeekOCR(BaseModel):
    def load(self):
        settings = get_settings()
        max_memory = {
            0: f"{settings.ocr_max_gpu_memory_gb}GB",
            "cpu": f"{settings.cpu_offload_memory_gb}GB"
        }

        self.model = AutoModel.from_pretrained(
            self.model_name,
            device_map="auto",
            max_memory=max_memory,
            torch_dtype=torch.bfloat16,
            cache_dir=self.cache_dir
        )

А метод predict() инкапсулирует всю сложность:

  • PDF → изображения;

  • resize больших картинок;

  • batch processing;

  • очистку CUDA-контекста.

Для роутеров и менеджера памяти это просто:

text = ocr_model.predict(image_bytes)

Почему это лучше, чем «просто импорт модели»

Без обёртки

model = AutoModel.from_pretrained(...)
# Как проверить состояние?
# Как выгрузить?
# Где конфигурация?
# Как считать idle time?

С обёрткой

if not model.is_loaded():
    model.load()

text = model.predict(data)

if model.get_idle_time_minutes() > 5:
    model.unload()

Все проблемы решены один раз и навсегда.

Итог: элегантность через абстракцию

Модуль app/models решает три ключевые задачи:

  • унификация — все модели имеют одинаковый API;

  • инкапсуляция — сложность библиотек спрятана внутри;

  • централизация — конфигурация и состояние под контролем.

Это позволяет:

  • легко добавлять новые модели;

  • прозрачно управлять памятью;

  • держать FastAPI-код чистым и читаемым.

Теперь переходим к нашей обертке напрямую, а именно, разберемся с роутерами.

Модуль app/routers: HTTP API Gateway

Если модуль app/models отвечает за бизнес-логику работы с нейросетями, то app/routers — это точка входа во всю систему. Именно здесь FastAPI превращается в полноценный HTTP-шлюз между внешним миром и AI-моделями.

Роутеры:

  • принимают HTTP‑запросы;

  • валидируют входные данные;

  • вызывают нужную модель;

  • возвращают результат в структурированном JSON.

Клиент
(HTTP / JSON)
    │
    ▼
┌─────────────────────────┐
│ app/routers/ocr.py      │
│                         │
│ 1. Валидация входа      │
│ 2. Получение модели     │
│ 3. model.predict()      │
│ 4. JSON ответ           │
└──────────┬──────────────┘
           │
           ▼
┌─────────────────────────┐
│ app/models/deepseek_ocr │
│   (OCR бизнес-логика)   │
└─────────────────────────┘

Принцип разделения ответственности

Здесь важно зафиксировать ключевую идею:

  • Роутер не знает, как работает модель — он просто вызывает predict();

  • Модель не знает про HTTP, JSON и FastAPI — она занимается только inference.

Благодаря этому API и ML-часть развиваются независимо.

Структура модуля: один роутер = один раздел API

app/routers/
├── ocr.py    # /ocr/*  — OCR
├── asr.py    # /asr/*  — Speech-to-Text
├── tts.py    # /tts/*  — Text-to-Speech
└── chat.py   # /chat/* — LLM

Каждый роутер:

  • имеет собственный URL-префикс;

  • отдельный Swagger-раздел;

  • singleton-экземпляр модели;

  • одинаковый набор endpoint’ов.

Регистрация в main.py выглядит максимально прозрачно:

app.include_router(ocr.router)
app.include_router(asr.router)
app.include_router(tts.router)
app.include_router(chat.router)

В Swagger UI это превращается в аккуратную, читаемую структуру:

📁 OCR
  ├ POST /ocr/extract-text
  ├ GET  /ocr/model-info
  └ POST /ocr/reload-model

 ASR
 TTS
 Chat

Анатомия роутера: на примере OCR

Рассмотрим типичный роутер по шагам.

1. Конфигурация роутера

router = APIRouter(
    prefix="/ocr",
    tags=["OCR (Optical Character Recognition)"]
)

Это:

  • задаёт URL-префикс;

  • группирует endpoint’ы в Swagger.

2. Singleton-экземпляр модели

ocr_model: DeepSeekOCR = None

def get_ocr_model() -> DeepSeekOCR:
    global ocr_model
    if ocr_model is None:
        ocr_model = DeepSeekOCR(...)
    return ocr_model

Почему это критично:

  • загрузка модели = 5–10 секунд;

  • потребление = 5–7 GB VRAM;

  • создавать модель на каждый запрос — гарантированный OOM.

Singleton решает это один раз и навсегда.

3. Основной endpoint

@router.post("/extract-text")
async def extract_text(file: UploadFile, normalize: bool = False):
    model = get_ocr_model()

    if not model.is_loaded():
        await asyncio.to_thread(auto_unload_old_models_if_needed, required_gb=2.5)
        await asyncio.to_thread(model.load)

    model.update_last_used()

    text = await asyncio.to_thread(model.predict, contents)

    return {"text": text, "status": "success"}

Что здесь происходит:

  1. Валидация входных данных;

  2. Получение модели;

  3. Ленивая загрузка при необходимости;

  4. Inference в отдельном потоке;

  5. Формирование JSON-ответа.

Ключевые паттерны, используемые в роутерах

1. Singleton Pattern

Один экземпляр модели на всё приложение:

  • экономия памяти;

  • мгновенные повторные запросы;

  • сохранение состояния между вызовами.

2. Единая структура endpoint’ов

Каждый роутер следует одному шаблону:

  • основной endpoint — функциональность;

  • /model-info — статус и метаданные;

  • /reload-model — принудительная перезагрузка.

Это даёт:

  • предсказуемый API;

  • простые клиенты;

  • единый мониторинг.

3. Ленивая загрузка (is_loaded())

Модель не загружается при старте сервиса:

class BaseModel(ABC):
    def is_loaded(self) -> bool: ...
    def load(self): ...
    def unload(self): ...
    def predict(self, *args, **kwargs): ...

4. Асинхронность без блокировки event loop

ML-inference — синхронная и тяжёлая операция. Если выполнять её напрямую, FastAPI «замирает».

Решение — asyncio.to_thread():

await asyncio.to_thread(model.load)
await asyncio.to_thread(model.predict, data)

Результат:

  • event loop остаётся свободным;

  • сервис принимает новые запросы;

  • throughput вырастает в несколько раз.

5. Единая обработка ошибок

Все роутеры:

  • логируют полный traceback;

  • возвращают клиенту понятный JSON.

Клиент видит:

{ "detail": "Ошибка при обработке файла" }

А сервер — полноценный лог для отладки.

Преимущества модульной структуры роутеров

  • Изолированность
    Можно отключить OCR, не затрагивая ASR или TTS.

  • Масштабируемость
    Новая модель = новый файл + include_router().

  • Читаемая документация
    Swagger становится интуитивным.

  • Переиспользование кода
    Общая логика вынесена в app/utils.py.

Итог: роутеры как фасад для AI-логики

Модуль app/routers реализует паттерн Facade:

  • скрывает сложность ML;

  • предоставляет простой HTTP-интерфейс;

  • остаётся лёгким и читаемым.

В связке с app/models это даёт:

  • чистую архитектуру;

  • стабильную работу;

  • основу для продакшн-сервиса.

В следующем разделе мы переходим к самому важному — управлению памятью и тому, как эта архитектура позволяет удерживать 4 нейросети в рамках 16 GB VRAM без падений.

Файл сборки приложения: app/main.py

Если app/models — это бизнес-логика работы с нейросетями, а app/routers — HTTP-интерфейс, то app/main.py — это точка входа и оркестратор всего приложения.

Именно здесь:

  • создаётся экземпляр FastAPI;

  • подключаются все роутеры;

  • настраиваются middleware (CORS);

  • инициализируются фоновые задачи;

  • регистрируются lifecycle-события (startup / shutdown).

uvicorn app.main:app
        │
        ▼
┌─────────────────────────────┐
│        app/main.py          │
│                             │
│ 1. FastAPI instance         │
│ 2. Middleware               │
│ 3. Routers                  │
│ 4. MemoryManager            │
│ 5. Lifecycle hooks          │
└─────────────────────────────┘

Общая структура main.py

Файл можно логически разделить на несколько блоков:

  1. настройка логирования;

  2. lifecycle management (startup / shutdown);

  3. создание FastAPI-приложения;

  4. middleware (CORS);

  5. подключение роутеров;

  6. служебные endpoints;

  7. entry point для запуска.

Такой порядок делает файл прозрачным и легко читаемым, даже при довольно насыщенной логике.

Lifecycle management: управление жизненным циклом приложения

Один из ключевых моментов — использование lifespan через@asynccontextmanager.

@asynccontextmanager
async def lifespan(app: FastAPI):
    # STARTUP
    ...
    yield
    # SHUTDOWN
    ...

Что происходит при запуске

При старте приложения:

  • логируется текущая конфигурация;

  • инициализируется и запускается MemoryManager;

  • подготавливаются фоновые задачи.

Что происходит при завершении

При остановке (Ctrl+C, SIGTERM):

  • корректно останавливается MemoryManager;

  • все загруженные модели принудительно выгружаются из памяти;

  • освобождаются GPU и CPU ресурсы.

STARTUP
 ├─ логирование конфигурации
 ├─ запуск MemoryManager
 └─ готовность принимать запросы

SHUTDOWN
 ├─ остановка MemoryManager
 ├─ выгрузка всех моделей
 └─ graceful shutdown

Почему это важно:

  • гарантированный cleanup ресурсов;

  • отсутствие утечек GPU-памяти;

  • корректное завершение в production-среде.

Глобальный MemoryManager

MemoryManager — это фоновый оркестратор, который следит за тем, какие модели используются, и автоматически выгружает простаивающие.

Он запускается один раз при старте приложения:

if settings.auto_unload:
    memory_manager = MemoryManager()
    await memory_manager.start()

И корректно останавливается при shutdown.

Важный момент: main.py не знает, как именно работает менеджер памяти. Он лишь:

  • запускает его;

  • останавливает;

  • доверяет ему контроль над моделями.

Это ещё один пример чистого разделения ответственности.

Создание FastAPI-приложения

app = FastAPI(
    title="FastAPI AI Models",
    description="...",
    version="1.0.0",
    lifespan=lifespan,
    docs_url="/docs",
    redoc_url="/redoc"
)

Что это даёт из коробки:

  • Swagger UI (/docs);

  • ReDoc (/redoc);

  • OpenAPI-схему (/openapi.json);

  • lifecycle hooks без костылей.

FastAPI здесь выступает не просто как HTTP-сервер, а как платформа для AI-сервиса.

Middleware: CORS

Для возможности работы с фронтендом или внешними клиентами подключается CORS:

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # для dev
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

В production это легко ограничивается конкретными доменами — всё централизовано в одном месте.

Подключение роутеров

app.include_router(ocr.router)
app.include_router(asr.router)
app.include_router(tts.router)
app.include_router(chat.router)

Это даёт важные преимущества:

  • модульность — любой роутер можно отключить одной строкой;

  • масштабируемость — новый AI‑модуль = новый роутер;

  • прозрачность — весь API виден сразу.

Служебные endpoints

/

Корневой endpoint — быстрая проверка, что сервис жив:

{
  "message": "FastAPI AI Models API",
  "docs": "/docs",
  "models": {
    "ocr": "/ocr/model-info",
    "asr": "/asr/model-info"
  }
}

/health

Health check для мониторинга, load balancer и Kubernetes:

  • статус приложения;

  • наличие GPU;

  • текущее потребление памяти.

Используется для:

  • liveness / readiness probes;

  • автоматического мониторинга.

/models/status

Самый полезный диагностический endpoint:

  • какие модели загружены;

  • сколько времени простаивают;

  • текущее состояние GPU-памяти.

Идеален для:

  • отладки OOM-проблем;

  • понимания нагрузки;

  • capacity planning.

Entry point для запуска

if __name__ == "__main__":
    uvicorn.run(
        "app.main:app",
        host="0.0.0.0",
        port=8000,
        reload=False
    )

Позволяет:

  • запускать приложение напрямую (python -m app.main);

  • использовать uvicorn или gunicorn в production.

Порядок инициализации приложения

При запуске происходит следующий поток:

  1. uvicorn app.main:app

  2. импорт модулей и конфигурации

  3. создание FastAPI instance

  4. подключение middleware

  5. регистрация роутеров

  6. lifespan STARTUP

  7. приложение готово принимать запросы

  8. lifespan SHUTDOWN при остановке

Итог: main.py как дирижёр оркестра

app/main.py — это дирижёр, который:

  • создаёт сцену (FastAPI);

  • подключает инструменты (роутеры);

  • управляет ресурсами (MemoryManager);

  • следит за порядком (lifecycle);

  • корректно завершает работу.

При этом сам файл остаётся компактным и читаемым, а вся сложность вынесена в специализированные модули.

Запуск и тестирование приложения

На этом этапе у нас уже есть:

  • настроенный сервер с GPU;

  • собранное FastAPI-приложение;

  • архитектура, менеджер памяти и все модели.

Теперь запускаем сервис и проверяем, что всё действительно работает так, как задумано.

Подготовка окружения

Открываем терминал на сервере (через VS Code Remote SSH или обычный SSH) и переходим в директорию проекта.

Шаг 1. Активация виртуального окружения

cd /home/fastapi_ai
source .venv/bin/activate

Если окружение активировано корректно, в начале строки появится:

(.venv) user@server:/home/fastapi_ai$

Шаг 2. Установка зависимостей

При первом запуске устанавливаем зависимости:

pip install -r requirements.txt

Установка займёт 5–10 минут, так как PyTorch и CUDA-библиотеки достаточно тяжёлые.

Запуск приложения

Базовый запуск (development)

Открываем первый терминал и запускаем сервис:

uvicorn app.main:app --host 0.0.0.0 --port 8000 --log-level info

Параметры:

  • app.main:app — путь к FastAPI instance;

  • --host 0.0.0.0 — слушаем все интерфейсы;

  • --port 8000 — порт сервиса;

  • --log-level info — читаемые логи.

Успешный запуск выглядит так:

Application startup complete.
Uvicorn running on http://0.0.0.0:8000
Запуск FastAPI AI Models...
Memory Manager запущен
Все роутеры подключены

Признаки, что всё в порядке:

  • нет ошибок в логах;

  • Memory Manager успешно стартовал;

  • приложение слушает порт.

Тестирование через curl

Открываем второй терминал (первый оставляем с логами).

1. Проверка доступности

curl http://localhost:8000/

Ответ:

{
  "message": "FastAPI AI Models API",
  "docs": "/docs",
  "models": {
    "ocr": "/ocr/model-info",
    "asr": "/asr/model-info",
    "tts": "/tts/model-info",
    "chat": "/chat/model-info"
  }
}

2. Health Check

curl http://localhost:8000/health | jq

Проверяем:

  • приложение живо;

  • GPU доступна;

  • память используется корректно.

3. Статус моделей

curl http://localhost:8000/models/status | jq

При первом запуске все модели будут выгружены — это нормально (lazy loading).

4. Тест OCR

curl -o test.jpg "https://via.placeholder.com/800x200/FFFFFF/000000?text=Hello+World+OCR"

curl -X POST "http://localhost:8000/ocr/extract-text" \
  -F "[email protected]" | jq

Первый запрос:

  • загружает OCR модель;

  • занимает 6–7 GB VRAM;

  • может длиться 10–20 секунд.

5. OCR + нормализация через LLM

curl -X POST "http://localhost:8000/ocr/extract-text?normalize=true" \
  -F "[email protected]" | jq

При этом автоматически подгружается Chat-модель.

6. Тест Chat

curl -X POST "http://localhost:8000/chat/simple" \
  -H "Content-Type: application/json" \
  -d '{"message": "Привет! Как дела?"}' | jq

7. Тест ASR и TTS

Аналогично проверяем:

  • /asr/transcribe — распознавание речи;

  • /tts/synthesize — синтез аудио в WAV.

Проверка авто-выгрузки моделей

Ничего не делаем 5+ минут, затем:

curl http://localhost:8000/models/status | jq

Модели будут автоматически выгружены, а GPU-память освобождена — это и есть ключевая цель всей архитектуры.

Публикация сервиса: systemd

Если на предыдущем этапе вы убедились, что приложение корректно запускается, модели загружаются и тестовые запросы отрабатывают без ошибок, можно переходить к завершающим шагам:

  1. перевести FastAPI‑приложение в постоянный продакшен‑режим;

  2. обеспечить автозапуск и автоматический рестарт при сбоях;

  3. подготовить сервис к публикации наружу под доменным именем.

Начнём с systemd — это самый простой и надёжный способ управлять сервисом на сервере без лишних технологий.

Запуск FastAPI через systemd

Почему systemd

systemd решает сразу несколько задач:

  • автозапуск приложения при старте сервера;

  • автоматический перезапуск при падениях;

  • централизованные логи;

  • контроль состояния сервиса (active / failed).

Для GPU-сервиса с нейросетями это дефолтный и рекомендуемый вариант.

Шаг 1. Останавливаем запущенное приложение

Если FastAPI сейчас запущено вручную через uvicorn — остановите его (Ctrl+C) и убедитесь, что порт 8000 свободен.

Шаг 2. Проверяем структуру проекта

Переходим в директорию проекта:

cd /home/fastapi_ai
pwd
ls -la

Убедитесь, что:

  • есть файл app/main.py;

  • виртуальное окружение .venv создано и содержит uvicorn.

Шаг 3. Создаём systemd unit-файл

Создаём сервис:

sudo nano /etc/systemd/system/fastapi-ai.service

Вставляем конфигурацию:

[Unit]
Description=FastAPI AI app with Uvicorn (GPU)
After=network.target

[Service]
Type=simple
User=root
Group=root
WorkingDirectory=/home/fastapi_ai

# Используем виртуальное окружение
Environment=PATH=/home/fastapi_ai/.venv/bin:/usr/local/bin:/usr/bin:/bin

# Явно указываем GPU
Environment=CUDA_VISIBLE_DEVICES=0

# Оптимизация аллокации CUDA памяти
Environment=PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128

ExecStart=/home/fastapi_ai/.venv/bin/uvicorn app.main:app \
  --host 0.0.0.0 \
  --port 8000 \
  --log-level info

Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

Важно про workers

Для GPU-приложений рекомендуется --workers 1. Несколько воркеров означают несколько процессов, каждый из которых будет:

  • иметь собственные экземпляры моделей;

  • дублировать потребление VRAM;

  • быстро привести к OOM.

Поэтому в данном проекте:

  • 1 worker = стабильность и контроль памяти.

Шаг 4. Перезагружаем systemd и запускаем сервис

sudo systemctl daemon-reload
sudo systemctl enable fastapi-ai
sudo systemctl start fastapi-ai

Шаг 5. Проверяем статус и логи

Проверяем состояние:

sudo systemctl status fastapi-ai

Ожидаемый результат:

● fastapi-ai.service - FastAPI AI app with Uvicorn
   Loaded: loaded (/etc/systemd/system/fastapi-ai.service)
   Active: active (running)

Смотрим логи в реальном времени:

sudo journalctl -u fastapi-ai -f

Вы должны увидеть знакомые сообщения:

  • запуск FastAPI;

  • инициализация Memory Manager;

  • подключение роутеров.

Шаг 6. Проверяем работу сервиса

Теперь приложение работает в фоне. Проверяем:

curl http://localhost:8000/
curl http://127.0.0.1:8000/health

Если ответы приходят — сервис стабильно работает и готов к проксированию через Nginx.

Управление сервисом (шпаргалка)

sudo systemctl stop fastapi-ai        # остановить
sudo systemctl start fastapi-ai       # запустить
sudo systemctl restart fastapi-ai     # перезапустить
sudo systemctl status fastapi-ai      # статус
sudo journalctl -u fastapi-ai -f      # логи

Что мы получили на этом этапе

К этому моменту у нас есть:

  • FastAPI‑приложение, работающее в продакшен‑режиме;

  • автозапуск при старте сервера;

  • автоматический рестарт при сбоях;

  • централизованные логи через systemd;

  • стабильная работа с GPU и памятью.

Следующий шаг — публикация сервиса наружу: настроим Nginx, привяжем домен и включим HTTPS, чтобы API стало доступно извне.

Публикация наружу: Nginx + домен + HTTPS

На этом этапе FastAPI-приложение уже стабильно работает через systemd и доступно на localhost:8000. Осталось сделать последний шаг — открыть сервис во внешний мир:

  • привязать доменное имя;

  • настроить Nginx как reverse-proxy;

  • включить HTTPS с помощью Let’s Encrypt.

Шаг 1. Установка Nginx и Certbot

На сервере устанавливаем всё необходимое:

sudo apt update
sudo apt install -y nginx certbot python3-certbot-nginx

Проверяем, что Nginx запущен:

sudo systemctl status nginx

Если сервис активен — можно двигаться дальше.

Шаг 2. Проверка DNS

Перед настройкой Nginx обязательно убедитесь, что домен уже указывает на IP сервера.

Проверить можно так:

ping your-domain.ru

или:

dig your-domain.ru +short

Если возвращается IP вашего сервера — DNS настроен корректно.

Шаг 3. Базовый конфиг Nginx (без HTTPS)

Сначала создаём простейший HTTP-конфиг, чтобы убедиться, что проксирование работает.

Переходим в директорию конфигураций:

cd /etc/nginx/sites-available

Создаём новый файл (название = домен):

sudo nano your-domain.ru

Минимальный конфиг без HTTPS:

server {
    listen 80;
    server_name your-domain.ru www.your-domain.ru;

    client_max_body_size 300M;

    location / {
        proxy_pass http://127.0.0.1:8000;

        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;

        proxy_buffering off;
    }
}

Активируем конфиг:

sudo ln -s /etc/nginx/sites-available/your-domain.ru /etc/nginx/sites-enabled/

Проверяем конфигурацию:

sudo nginx -t

Перезапускаем Nginx:

sudo systemctl reload nginx

Теперь проверьте в браузере:

http://your-domain.ru

Если FastAPI отвечает — значит проксирование работает корректно.

Шаг 4. Подключаем HTTPS через Certbot

Когда HTTP-доступ работает, можно включать HTTPS.

Запускаем Certbot:

sudo certbot --nginx -d your-domain.ru -d www.your-domain.ru

Certbot:

  • автоматически выпустит SSL-сертификат;

  • изменит конфиг Nginx;

  • настроит редирект с HTTP → HTTPS.

После успешного выполнения сайт станет доступен по:

https://your-domain.ru

Финальный боевой конфиг Nginx (пример)

После работы Certbot конфигурация будет выглядеть примерно так (пример из реального продакшена):

server {
    server_name yakvenalexx-habr.ru www.yakvenalexx-habr.ru;

    # Таймауты для долгих запросов (OCR, ASR, TTS)
    client_body_timeout 1800s;
    proxy_connect_timeout 1800s;
    proxy_send_timeout 1800s;
    proxy_read_timeout 1800s;
    send_timeout 1800s;

    client_max_body_size 300M;

    location / {
        proxy_pass http://127.0.0.1:8000;

        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;

        # Отключаем буферизацию (важно для streaming / long inference)
        proxy_buffering off;

        # WebSocket support (на будущее)
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
    }

    listen 443 ssl;
    ssl_certificate /etc/letsencrypt/live/yakvenalexx-habr.ru/fullchain.pem;
    ssl_certificate_key /etc/letsencrypt/live/yakvenalexx-habr.ru/privkey.pem;
    include /etc/letsencrypt/options-ssl-nginx.conf;
    ssl_dhparam /etc/letsencrypt/ssl-dhparams.pem;
}

server {
    listen 80;
    server_name yakvenalexx-habr.ru www.yakvenalexx-habr.ru;

    return 301 https://$host$request_uri;
}

Почему именно такие настройки

  • Большие таймауты
    Нейросети могут обрабатывать запросы десятки секунд (OCR, ASR, TTS).

  • client_max_body_size 300M
    Для PDF, аудио и длинных файлов.

  • proxy_buffering off
    Убирает задержки при длинных ответах и стриминге.

  • WebSocket‑заголовки
    Не обязательны сейчас, но полезны, если позже появится streaming‑чат.

Проверка

Проверяем сертификат:

sudo certbot certificates

Проверяем автообновление:

sudo certbot renew --dry-run

Проверяем доступность:

curl https://your-domain.ru/health

Что мы получили в итоге

На этом этапе:

  • FastAPI работает как systemd-сервис;

  • Nginx выступает reverse-proxy;

  • домен привязан;

  • HTTPS включён;

  • API доступно извне;

  • готово к использованию в продакшене.

Тестируем API в Swagger

Теперь убедимся, что всё работает как часы: откроем Swagger UI и протестируем каждый эндпоинт.

В моём случае Swagger доступен по адресу: https://yakvenalexx-habr.ru/docs

На момент прочтения статьи ссылка скорее всего неактивна — VPS использовался только для демонстрации в этой статье.

Приступаем к тестам!

  1. Перейдите по ссылке — увидите интерактивную документацию со всеми эндпоинтами

  2. Нажимайте "Try it out" для каждого метода

  3. Заполняйте параметры и жмите Execute

  4. Проверяйте ответы в правой панели — статус 200 и корректный JSON?

Тестируем Qwen модель (чат)
Тестируем Qwen модель (чат)
Фото для теста OCR
Фото для теста OCR
Результат теста с нормализацией через Qwen
Результат теста с нормализацией через Qwen
Теперь озвучим прочитанный через OCR текст
Теперь озвучим прочитанный через OCR текст
Распознаем полученное аудио через Whisper.
Распознаем полученное аудио через Whisper.

Если все тесты проходят — сервис готов к бою!

Выводы

В этой статье мы прошли полный путь — от идеи до рабочего продакшен-сервиса с локальными нейросетями.

В итоге у нас получилось:

  • полноценное мультимодальное AI‑приложение (OCR, ASR, LLM, TTS);

  • работа на одной GPU с 16 GB VRAM без облачных API;

  • осознанная архитектура на FastAPI + PyTorch + Transformers;

  • умное управление памятью, без CUDA Out of Memory;

  • стабильный продакшен‑запуск через systemd;

  • публикация наружу через Nginx + домен + HTTPS.

По сути, мы собрали локальный AI-сервис «под ключ», который:

  • не отправляет данные на сторонние сервера;

  • имеет предсказуемую стоимость;

  • легко масштабируется и расширяется;

  • подходит как для pet‑проектов, так и для бизнес‑задач.

Где такой сетап действительно полезен

Подобная архитектура хорошо подходит для:

  • корпоративных сервисов, где важна конфиденциальность данных;

  • внутренних инструментов (документооборот, распознавание речи, ассистенты);

  • стартапов, которым дорого масштабироваться на облачных API;

  • экспериментальных проектов, где важен полный контроль над моделями;

  • образовательных и R&D‑задач.

Главный вывод, который я хотел донести: локальные нейросети — это больше не «дорого и сложно». При грамотной архитектуре и управлении ресурсами они становятся вполне доступным и практичным инструментом.

Что можно улучшать дальше

Если развивать этот проект, логичные следующие шаги:

  • добавить streaming‑ответы для Chat и ASR;

  • прикрутить очередь задач (например, Redis + background workers);

  • добавить авторизацию и rate limiting;

  • вынести модели на отдельные GPU при росте нагрузки;

  • подключить метрики и мониторинг (Prometheus / Grafana).

Основа для всего этого уже заложена.

Обратная связь и обсуждение

Мне действительно важно получить обратную связь от вас:

  • было ли полезно;

  • какие моменты стоило раскрыть глубже;

  • какие темы по локальным нейросетям интересны дальше.

Если хотите обсудить архитектуру, задать вопросы или посмотреть другие практические проекты — приглашаю в мой Telegram-канал «Лёгкий путь в Python». Там уже более 5000 участников, я регулярно публикую исходный код, разборы и дополнительные материалы к статьям.

Ссылка на исходники проекта — в начале статьи, всё в открытом доступе.

Спасибо, что дочитали до конца. Надеюсь, этот материал поможет вам увереннее чувствовать себя в мире локальных AI-решений и не бояться поднимать нейросети на собственном железе.

Почасовая аренда серверов с GPU-картами

Серверы с видеокартами NVIDIA — от доступных моделей начального уровня до профессиональных Tesla, в дата-центрах в России и Европе.

При длительной аренде — скидка до 35 %.

Посмотреть конфигурации →

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  • 07.07.26 18:00 robertalfred175

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  • 07.07.26 18:01 robertalfred175

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  • 16.07.26 21:38 patricialovick86

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  • 30.07.26 00:04 Ahmed

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

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  • 23.08.26 20:02 leslieyee

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  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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