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Н Новости

Домашний ИИ и пути развития: одна Tesla V100 32 ГБ в 2026 году

Старая серверная карта Tesla V100 на 32 ГБ до сих пор стоит как одна современная видеокарта среднего класса, а памяти у неё столько, что в неё целиком помещается модель на 30 млрд параметров. Я собрал на ней домашний сервер для локальных моделей, прогнал замеры скорости и по дороге выяснил, что половина моих “выводов” была ошибками измерения. Ниже цифры, методика, грабли и два примера из реальных рабочих проектов, где локальные модели помогали (и где не помогли).

Все скрипты и сырые результаты лежат в репозитории: github.com/DmNep/llm-bench-v100.

Зачем вообще локальные модели

В моих проектах причины были разные, и ни в одном из них это не была “экономия на подписке”:

  • Приватность. В проекте по восстановлению данных 1С из повреждённого файла базы (после шифровальщика) в данных были настоящие персональные данные живых людей. Правило проекта: агентам по умолчанию дают работу со структурами, смещениями и идентификаторами, а не со сводными таблицами, и эти данные без необходимости не уходят в облачные сервисы. Локальная модель работает на моём компьютере, поэтому данные не приходится отправлять в облако: разбор идёт там же, где они лежат.

  • Пропускная способность. Задач вида “есть ли в повреждённом файле данные про X” были десятки, каждая отрицательная проверка стоит часы, и перебор удобно отдать локальному воркеру.

  • Чувствительное сырьё. В другом проекте нужно было разобрать видеозапись с голосовыми пояснениями: звук я распознавал локально, на своей машине, чтобы он никуда не уходил.

  • Разработка и тестирование на 1С, криптография и криптовалюты. Данные, с которыми я работаю при проектировании, разработке и тестировании функционала на 1С, а также в проектах по криптографии и криптовалюте, чувствительные и никуда не должны уходить. Поэтому у меня стоит локальная модель (подробнее ниже).

Стенд

  • GPU: Tesla V100-SXM2 32 ГБ (архитектура Volta, compute capability 7.0) на переходной плате SXM2 → PCIe. Драйвер NVIDIA 580.

  • ОС: Ubuntu 26.04. Оперативная память: в основных замерах Ollama и llama.cpp 30 ГБ (free -h показывает 28Gi, потому что Linux считает в гибибайтах: 1 ГиБ = 1,074 ГБ; в статье я везде перевёл в гигабайты; это важно, см. ниже), для Strata я потом докупил и поставил 96 ГБ (2×32 + 2×16 ГБ, DDR4-2666), см. раздел про Strata. Процессор у основной машины слабый (4 ядра), у стенда для Strata Ryzen 7 PRO 4750G (8 ядер), карта на PCIe 3.0 x16.

  • Софт: два движка на одних и тех же файлах GGUF: Ollama 0.34.2 и отдельно собранный llama.cpp (llama-server; CUDA 12.9, gcc-14, флаг CMAKE_CUDA_ARCHITECTURES=70). llama.cpp понадобился сначала для 128K контекста, потом я прогнал на нём все модели.

  • Особенность: сервером я периодически управлял без SSH, через звуковой канал. Поначалу VPN часто ронял сеть, и обычное подключение пропадало, а звук между аудио-выходом ноутбука и входом сервера работал всегда (команды передаются как звук, около 1200 бод). Это не обязательная часть истории, но объясняет, почему часть команд я писал короткими и без интерактива. Сам инструмент я вынес в отдельный проект: github.com/DmNep/x_connector.

Как я измерял

Скрипт bench.ps1 ходит в API Ollama и для каждой модели и каждого размера запроса делает три прогона:

  • в каждом прогоне случайный текст и случайный идентификатор в начале, чтобы кэш промпта Ollama не превратил чтение запроса в “мгновенное” (это самая частая ошибка в чужих замерах);

  • размер запроса берётся из prompt_eval_count ответа сервера, а не “на глаз по словам”: если Ollama обрезала запрос, строка помечается и в медианы не идёт;

  • генерация фиксированной длины (128 токенов) без случайности: модель всегда берёт самое вероятное продолжение;

  • итоговое число — это медиана из трёх повторов; между моделями — предыдущая выгружается из памяти.

Для llama.cpp то же самое делает bench-llamaserver.ps1 через эндпоинт /completion (поля tokens_evaluated и timings, кэш промпта выключен). Окна те же, что у соответствующих тегов Ollama. Кэш ключей и значений (KV) у моделей на 32K стоит в f16, как по умолчанию у Ollama; у Qwen3-30B на 128K пришлось взять q8_0, иначе она не влезает в карту (подробнее ниже, это важно для сравнения).

Результаты

Как читать таблицы. Токен — это кусочек текста, на которые модель режет всё, что читает и пишет (около 3–4 символов английского текста; русские слова в моём тесте давали около 2 токенов на слово). Окно — это максимальный размер контекста, на который модель настроена (в Ollama параметр num_ctx, в llama.cpp -c): сколько токенов в сумме помещается в “памяти” модели, считая запрос и ответ. Столбцы 1K…115K — это размер запроса в тесте, то есть сколько контекста уже заполнено в момент замера (1K около 1 100 токенов, 67K около 66 700, 115K около 118 000). Прочерк значит, что запрос такого размера не влезает в окно модели.

Движки идут рядом, на одних и тех же файлах GGUF. Строка “разница” — это отклонение llama.cpp от Ollama в процентах: минус значит, что llama.cpp медленнее, плюс что быстрее.

Скорость генерации (токенов в секунду) при запросе указанного размера в контексте:

Модель

Движок

1K

4K

16K

25K

33K

67K

115K

llama3.2:1b (Q8_0)

Ollama

288

280

248

223

-

-

-

llama.cpp

303

292

245

226

-

-

-

разница

+5%

+5%

−1%

+1%

-

-

-

Qwen3-30B-A3B-Thinking (Q4_K_M) ⁴

Ollama

133

125

105

94,8

86,2

63,7

-

llama.cpp ³

110

99,0

69,8

59,0

50,7

32,6

21,5

разница

−17%

−21%

−33%

−38%

−41%

−49%

-

Gemma 4 31B (Q4_K_M)

Ollama ¹

56,4

54,2

50,9

49,6

45,5

-

-

llama.cpp

29,9

29,2

28,1

27,5

26,7

-

-

разница

−47%

−46%

−45%

−45%

−41%

-

-

Qwen3-Coder-30B-A3B (Q8_0)

Ollama

45,5

44,4

41,6

40,1

-

-

-

llama.cpp ²

35,7

36,4

34,0

32,9

-

-

-

разница

−22%

−18%

−18%

−18%

-

-

-

Gemma 4 31B QAT (Q4_0)

Ollama

33,8

32,3

31,0

30,2

-

-

-

llama.cpp

33,4

32,5

31,1

30,3

-

-

-

разница

−1%

+1%

0%

0%

-

-

-

¹ Цифры Ollama для этого тега недостоверны: она показывает 2,1 ГБ в памяти при весе файла 20,4 ГБ, а скорость 56 ток/с выше физического предела плотной 31B-модели на 900 ГБ/с (около 44 ток/с). Тот же файл на llama.cpp дал 30 ток/с и 23,7 ГБ видеопамяти, и это правдоподобно. Почему Ollama показала другое, выяснить не удалось.

² Файл Qwen3-Coder в Q8_0 весит около 32 ГБ и вместе с кэшем в карту не влезает: llama-server падает с ошибкой выделения буфера. Здесь 8 слоёв экспертов оставлены в оперативной памяти (--n-cpu-moe 8); число слоёв не подбиралось, при меньшем числе может быть быстрее.

³ Настройки кэша разные: Ollama держит KV-кэш в f16 (31 ГБ видеопамяти на окно 131072), а llama.cpp я запускал с q8_0 (24,9 ГБ), иначе окно 131072 вместе с моделью в карту не влезает. Работа с квантованным кэшем на длинном контексте стоит скорости, поэтому разрыв растёт с размером запроса. Сравнение здесь — это сравнение “движок + настройки”, а не одних движков. 115K (около 118 тысяч токенов) я мерил только на llama.cpp.

⁴ “A3B” в названии означает 3 млрд активных параметров из 30: это модель типа MoE (“смесь экспертов”), на каждое слово у неё работает лишь небольшая часть параметров. Подробнее об этом ниже, в пояснении после таблиц.

Скорость чтения запроса (prefill), токенов в секунду:

Модель

Движок

1K

4K

16K

25K

33K

67K

115K

llama3.2:1b (Q8_0)

Ollama

18 181

19 476

13 160

10 721

-

-

-

llama.cpp

16 791

16 447

11 497

9 411

-

-

-

разница

−8%

−16%

−13%

−12%

-

-

-

Qwen3-30B-A3B-Thinking (Q4_K_M)

Ollama

1 298

1 311

1 170

1 074

985

743

-

llama.cpp ³

1 177

1 309

1 163

1 056

972

729

529

разница

−9%

0%

−1%

−2%

−1%

−2%

-

Gemma 4 31B (Q4_K_M)

Ollama ¹

409

553

554

518

484

-

-

llama.cpp

497

600

576

528

493

-

-

разница

+21%

+8%

+4%

+2%

+2%

-

-

Qwen3-Coder-30B-A3B (Q8_0)

Ollama

812

989

933

876

-

-

-

llama.cpp ²

414

524

524

502

-

-

-

разница

−49%

−47%

−44%

−43%

-

-

-

Gemma 4 31B QAT (Q4_0)

Ollama

571

698

676

624

-

-

-

llama.cpp

484

600

574

527

-

-

-

разница

−15%

−14%

−15%

−16%

-

-

-

Ollama или llama.cpp? На плотных моделях с кэшем f16 генерация почти одинакова: QAT-версия Gemma и маленькая llama3.2:1b отличаются на 1–5%. Заметная разница там, где отличаются настройки или где цифры подозрительны:

  • Qwen3-30B на большом окне: у Ollama быстрее генерация, и разрыв растёт с длиной запроса (−17% на 1K, −49% на 67K), а чтение запроса почти одинаковое (в пределах 2%). Причина в кэше q8_0 у llama.cpp (см. сноску ³). Обратная сторона: llama.cpp честно вернул ошибку, когда запрос не влез, а Ollama молча обрезает его вдвое (см. ниже).

  • Qwen3-Coder: у Ollama быстрее и генерация (−18…−22%), и чтение запроса (почти вдвое), но у меня часть экспертов ушла в оперативную память, и это настройка, а не свойство движка.

  • Gemma 4 31B Q4_K_M: по Ollama она “быстрее” вдвое, но это цифры из сноски ¹, которым верить нельзя.

Что из этого видно:

  • Мало активных параметров = быстро. Модели бывают двух устройств. У плотной модели (такая Gemma 31B) на каждое слово работают все параметры. MoE (Mixture of Experts, “смесь экспертов”) состоит из множества небольших блоков-“экспертов”, и на каждое слово включаются лишь несколько из них, остальные в этот момент простаивают. Поэтому Qwen3-30B-A3B (MoE: 30 млрд параметров всего, из них 3 млрд активных, это и значит “A3B” в названии) выдаёт 110–133 ток/с, а плотная Gemma 31B 30–34 ток/с при сопоставимом размере файла. На такой карте MoE-модели выигрывают в 3–4 раза.

  • Длинный контекст стоит скорости. У Qwen3-Thinking в Ollama генерация падает с 133 до 64 ток/с при росте запроса с 1K до 67K, а чтение запроса с 1 300 до 743 ток/с; на llama.cpp с кэшем q8_0 с 110 до 21,5 ток/с на запросе в 118 тысяч токенов. Плотные модели замедляются мягче: у Gemma 4 31B с 30 до 27 ток/с на 33K.

  • Квант Q8 не бесплатен. Qwen3-Coder в Q8_0 (32,5 ГБ) почти заполняет карту и даёт 36–46 ток/с на коротком запросе (в зависимости от движка); Qwen3-Thinking в Q4_K_M при том же числе параметров вдвое-втрое быстрее. Эти две модели разные, поэтому прямо сравнивать их нельзя, но порядок величин показывает цену точности.

Strata: MoE в 125 млрд параметров на одной V100

Отдельный движок Strata запускает модель Qwen3.8-Flash-Next (MoE, около 125 млрд параметров, около 6 млрд активных) на обычной видеокарте. Идея: внимание и самые частые эксперты лежат в видеопамяти (у меня кэш на 9 489 экспертов, 23 ГБ), остальные эксперты в оперативной памяти, а недостающие подкачиваются по PCIe. Поэтому скорость сильно зависит от того, сколько экспертов помещается в оперативку.

Я мерил три конфигурации одного и того же сервера:

Конфигурация

Модель

Чтение запроса, ток/с

Генерация, ток/с

64 ГБ DDR4-2133, 4 ядра, V100 на PCIe x8

IQ2_XS (2 бита, 39 ГБ)

595–1 570 (на 1K–25K)

71–73

48 ГБ ОЗУ, бюджет экспертов 22,5 ГБ

UD-Q4_K_XL (4 бита, 111 ГБ)

87 (1K), 547 (25K)

28–40

96 ГБ ОЗУ, все эксперты в памяти

UD-IQ4_XS (4 бита, 94 ГБ)

310 (1K), 1 236 (25K), 1 215 (117K), 1 065 (239K)

55–61 (до 66 с размышлением)

96 ГБ ОЗУ, бюджет 69 из 77 ГБ

UD-Q4_K_XL

229, 1 112, 1 110, 982

47–52

Что такое “бюджет”. Это настройка Strata --resident-budget-gib: сколько гигабайт весов экспертов она постоянно держит в оперативной памяти. Всё, что в бюджет не вошло, остаётся в файлах модели на диске (NVMe) и читается оттуда по мере надобности. Это не файл подкачки: файл подкачки — запасное место для данных обычных программ, а Strata читает сами файлы модели, подключённые к процессу как отображаемые в память. Пока хватает свободной оперативки, прочитанные куски оседают в кэше файловой системы и повторно с диска не читаются. Файлом подкачки процесс почти не пользуется: у меня на сервере 0,2 ГБ при 85 ГБ занятой памяти. Запись “69 из 77 ГБ” значит: у модели UD-Q4_K_XL эксперты занимают 77 ГБ, в оперативке я держу 69, остальные 8 читаются с диска. У UD-IQ4_XS бюджет равен всем 59 ГБ экспертов, за весами экспертов диск обращаться не приходится. Чем меньше бюджет, тем чаще обращения к диску и тем медленнее ответ.

На 96 ГБ эксперты Q4_K_XL целиком в оперативной памяти не помещаются: пик занятой памяти у службы Strata и так почти равен всей оперативке сервера, поэтому часть экспертов приходится читать с диска. Я проверил бюджет 64, 66 и 68 ГиБ на рабочем контексте: прирост есть, но выходит на плато уже на 66 ГиБ (43,9 ток/с против 42,5 на исходных 64 ГиБ; 68 ГиБ уже ничего не добавляет, попадания в кэш экспертов те же 93,7%). Поставил рабочим значением 66 ГиБ, но даже на нём около 6 ГБ экспертов всё ещё читаются с диска.

Проверить это быстрее, чем ждать переходник, получилось так: вытащил одну планку на 16 ГБ из сервера и поставил вместо неё планку на 32 ГБ с другого своего ПК — чисто ради эксперимента, довёл сервер до 112 ГБ и поставил бюджет на все 77 ГБ экспертов Q4_K_XL, без единого обращения к диску. Результат неожиданный: генерация почти не изменилась (42,0 ток/с против 43,9 на бюджете 66 ГиБ при 96 ГБ, в пределах шума замера), и попадания в кэш экспертов тоже не выросли (92,8% против 93,7%). При этом кэш “горячих” экспертов на видеокарте (expert_slots, expert_cache_mib) остался ровно тем же при любом бюджете ОЗУ — он упирается в объём видеопамяти (32 ГБ), а не в оперативку. Похоже, кэш файловой системы Linux и так неплохо держал недостающие экспертов, и гарантированная “резидентность” в бюджете Strata реального выигрыша не дала: узкое место сейчас не в оперативке, а в видеопамяти.

С учётом этого эксперимента ставка на вторую V100 выглядит правильнее, чем на память: скоро должен прийти переходник на два места SXM2 с NVLink, вторая карта даст ещё 32 ГБ видеопамяти под кэш горячих экспертов — то есть добавит именно туда, где сейчас реальный потолок. Это пока прогноз, а не замер.

Что тут видно:

  • Оперативка важнее, чем кажется. Одна и та же модель (UD-Q4_K_XL) на том же сервере при переходе с 48 на 96 ГБ стала читать запрос в 2 раза быстрее (1 112 против 547 ток/с на 25K токенов) и генерировать на 31–87% быстрее. Мой прогноз по кривой “бюджет экспертов → скорость” (+33–60%) оказался слишком осторожным: он не учитывал, что остаток экспертов целиком ложится в кэш файловой системы.

  • Скорость генерации почти не зависит от длины запроса: у UD-IQ4_XS 55–61 ток/с от 25K до 239K токенов, у UD-Q4_K_XL 47–52. Читается запрос в 239 тысяч токенов за 3,7–4 минуты.

  • Меньший квант быстрее на 10–15% и в моём случае умеет картинки (энкодер зрения подключён к IQ4_XS, к Q4_K_XL по документации нет). Качество ответов я не сравнивал: это только скорость.

  • В документации Strata (docs/NVIDIA_V100.md) для V100-PCIE 32 ГБ, процессора i9-7960X и той же модели UD-IQ4_XS указана генерация 38–51 ток/с (в README Strata цифр для V100 нет: карта там числится в экспериментальных). Мои 55–61 ток/с (до 66 с размышлением) выше этого диапазона, но у них другая машина (другой процессор, другая память), так что это не опровержение, а иллюстрация того, как много значит память.

  • Первый запрос после старта медленнее (24 против 37–43 ток/с): кэш экспертов прогревается, попадания в него растут с 65% до 96%.

Оговорки: один день и один стенд, 2–3 повтора на точку, разброс генерации до 15–20%; у двух 4-битных вариантов разный бюджет экспертов (IQ4_XS все 59 ГБ, Q4_K_XL 69 из 77), поэтому сравнивать их нужно с оговоркой. Расширение контекста за обученные 262K (YaRN до 512K и 1M) я мерил дважды. На 48 ГБ ОЗУ: 512K около 29 ток/с на генерации, 1M около 17 ток/с и 50 минут на чтение запроса в миллион токенов. На 96 ГБ ОЗУ с бюджетом 66 ГиБ: 512K — генерация 40,2 ток/с, попадания в кэш экспертов 93,4%; 1M — генерация 29,9 ток/с, попадания 87,9%, чтение миллионного запроса 530 ток/с (около 32 минут). Память заметно решает и здесь: почти на 40% быстрее на 512K и почти вдвое на 1M, хотя попадания в кэш на самом длинном контексте проседают — туда уходит больше памяти под KV-кэш.

Грабли

1. Ollama молча обрезает переполнивший запрос

История получилась показательной, потому что я сначала ошибся. Я создал тег с num_ctx 131072, ollama ps честно показывал окно 131072 и 100% GPU, 31 ГБ памяти. Стресс-тест запросом “на ~123 тысячи токенов” с “иглой” в конце (это проверка “needle in a haystack”: в длинный текст вставляют короткую приметную фразу и просят модель её процитировать — если найдёт, значит реально видит весь контекст, а не только часть) дал prompt_eval_count = 65538. Повторил с окном 98304 и получил 49154. Вывод напрашивался: у Ollama жёсткий потолок на 65536 токенов, дальше обрезка.

На самом деле числа 65538 и 49154 — это ровно половина окна плюс два токена. Ollama при переполнении окна молча оставляет только половину. А переполнялось оно потому, что мой тестовый запрос на деле содержал не 123 тысячи, а 659 тысяч токенов: я прикидывал токены по числу слов, а русские слова в моём тесте давали около 2,2 токена на слово, а не один. Выяснилось это, когда llama-server (llama.cpp) на тот же запрос честно вернул ошибку 400: request (659107 tokens) exceeds the available context size (131072 tokens).

После этого я сделал корректный запрос в 66 709 токенов в окно 131072: Ollama прочитала его целиком, обрезки не было. Выводы, которые стоит запомнить:

  • токены считать по prompt_eval_count, а не “на глаз”;

  • если prompt_eval_count равен примерно num_ctx / 2 + 2, значит запрос обрезан, а не обработан;

  • ollama ps показывает запрошенное окно, а не реально использованное;

  • проверять память “иглой”: фраза в самом начале и в самом конце запроса, и просить процитировать обе.

2. Контекст в интерфейсе клиента не равен контексту в Ollama

Строка состояния клиента (Qwen Code) показывала “262.1k Context”, а у модели в Ollama окно было 32768. Это число берётся из метаданных модели, а не из настроек сервера. Резать задачи нужно под реальное окно, иначе середина запроса молча теряется. Снаружи это выглядит как “зависание” или “модель потеряла нить”: на долгих сессиях в логах Ollama видно truncated = 1, а на клиенте Model stream ended after a tool result without visible progress. Сервер при этом отвечает за секунды, лечится это увеличением окна и более короткими пакетами, а не перезапуском сервера.

3. Настоящие 128K: llama.cpp и квантованный KV-кэш

Чтобы получить честные 131072, я собрал llama.cpp из исходников. На V100 есть три особенности:

  • CUDA 13 больше не поддерживает архитектуру Volta, нужна ветка 12.x (я взял 12.9);

  • системный gcc в Ubuntu 26.04 — это версия 15, CUDA 12.9 её не поддерживает, пришлось ставить gcc-14 и указывать -DCMAKE_CUDA_HOST_COMPILER=g++-14;

  • сборка на 4 ядрах заняла около 18 минут.

Запуск: llama-server -c 131072 -ngl 99 -np 1 -fa on -ctk q8_0 -ctv q8_0 --jinja. Ключевые флаги: -np 1 (иначе контекст делится между параллельными слотами) и квантованный KV-кэш q8_0 (он требует flash attention -fa on).

Результат: окно 131072 заработало, запрос в 118 576 токенов обработан без обрезки, обе “иглы” (в самом начале и в самом конце) найдены. Скорость: чтение запроса ~529 ток/с, генерация ~21,5 ток/с на таком длинном контексте (полный замер выше). Память: 24,9 ГБ вместо 31 ГБ у Ollama на том же окне, потому что KV-кэш хранится в q8_0. Цена этой экономии: на коротких запросах скорость ниже, чем у Ollama (110 против 133 ток/с), а на длинных заметно ниже (33 против 64 ток/с на 67K). Если окно меньше и кэш в f16 помещается, разницы между движками почти нет.

4. Одна карта на всех

Две модели одновременно на одной V100 не живут: 31 ГБ у Ollama плюс 25 ГБ у llama-server больше 32 ГБ. Работа последовательная: перед запуском одного движка другой нужно остановить.

5. Оперативная память: записанное и реальное

В заметках у сервера были записаны цифры 43 и 48 ГБ оперативки, а free -h показал 28Gi, то есть около 30 ГБ. Это тот случай, когда лучше проверить dmidecode, чем верить записи.

Потом я докупал память, и тут стало интереснее. Сбойные планки тихо портят систему. С одной из планок в Linux начали падать sudo, systemctl и dpkg (segfault), хотя диск был цел: испортился образ библиотек в оперативке. memtester в Linux проверил лишь часть объёма и пропускал ошибки; настоящий приговор вынес Memtest86+ с загрузочной флешки: на сбойной планке 716 ошибок подряд на втором проходе (первый проход был чистым: дефект плавающий). Памяти надо давать минимум два полных прохода Memtest86+, а не один. Итог: после замены сбойной планки 96 ГБ (четыре планки, режим DDR4-2666) прошли 2 прохода без ошибок.

6. Cline и потолок контекста: сжатие “пропускалось”, а запрос упёрся в окно модели

Агентом в VS Code у меня работает Cline, модель отдаёт Strata с окном 262 144 токена. Одна из задач шла двое суток (больше 3 600 сообщений) и однажды остановилась с ошибкой от сервера: prompt (262942 tokens) leaves no room to answer in the context (262144); requests are never truncated. Сервер честно отказался принять запрос, а не обрезал его молча (в отличие от Ollama, см. выше). Сам Cline при этом показывал в шапке 261.9k из 128.0k, то есть окно было настроено на 128 тысяч, но реальный контекст ушёл за его пределы вдвое, а в логе повторялась строка Compaction skipped.

Что я выяснил, разобрав расширение (версия 4.1.23):

  • у Cline нет настройки порога сжатия в процентах; она решает по размеру окна модели, которое записано в настройках, и по стратегии (agentic через модель с откатом на basic);

  • сжатие может “пропускаться”: оно запустилось, но убирать из истории нечего (последние сообщения сами огромные, например длинные выводы команд), и контекст продолжает расти;

  • размер окна лежит не там, где я искал: Cline 4.x при первом запуске переносит его в ~/.cline/data/settings/models.json, и правка старого globalState.json ничего не давала (в интерфейсе оставалось 100K). Менять надо именно models.json или поле в самом интерфейсе;

  • на 128K окно и 262K реальный контекст расходились, потому что сжатие не срабатывало, а не из-за порога.

Что сделал: поставил окно 200 000 (с запасом ниже серверных 262 144 на ответ до 32 тысяч токенов и вывод инструментов), старую задачу закрыл, а состояние передал в новую через заметку в Obsidian (цель, правила, что сделано, что дальше), потому что встроенную команду /newtask на переполненной задаче не запустить: ей нужен ответ модели на тот же переполненный запрос. В новой задаче прошу агента не выводить большие файлы целиком (Select-Object -First, -Tail, Select-String), потому что раздувают контекст именно они.

7. Безопасность: что уходит наружу из клиента и “удобных” утилит

Локальная модель решает вопрос “где лежат данные”, но клиенты вокруг неё живут своей жизнью. Я проверил два инструмента, которыми пользуюсь каждый день.

  • Cline. Запросы к модели идут только на 127.0.0.1 (Strata через SSH-туннель), но телеметрия расширения по умолчанию включена: нажатия кнопок, выбранные модель и провайдер, имена вызванных MCP-инструментов (без значений аргументов), коды завершения команд, обрезанные тексты ошибок API и исключения со стеком (в них попадают пути вроде C:\Users\…), идентификатор устройства. Текст диалога и файлов в просмотренных событиях я не нашёл, но разобрал не весь код. Я выключил телеметрию (telemetrySetting: disabled и telemetryOptOut: true) и удалил из настроек облачного провайдера: он остался от прошлой конфигурации и при случайном переключении унёс бы код проекта наружу.

  • Переключатель раскладки (Caramba Switcher). По лицензионному соглашению он отправляет в Google Analytics только служебную статистику (версия, идентификатор установки, язык, события вроде “клик по интерфейсу”); набранный текст не передаётся. Я пошёл дальше: поднял прямое TLS-соединение с известным именем хоста и прочитал предъявленный сертификат — единственное замеченное соединение шло на собственный CDN производителя (неожиданно, но не подозрительно, захостенный на московском IP “Мегафона”), а не на что-то постороннее; отдельного соединения к Google Analytics не поймал ни при запуске, ни при живом клике по интерфейсу. А вот заглянуть внутрь зашифрованного трафика не вышло: SSLKEYLOGFILE — соглашение из мира OpenSSL и браузеров, закрытые Windows-программы обычно сидят на системном Schannel и его не замечают, а ставить MITM с собственным корневым сертификатом ради одной утилиты показалось избыточным. Раз до конца убедиться не получилось — снёс программу (заодно нашёл, чем она стучится на самом деле: обычная библиотека автообновлений WinSparkle).

  • Страховка на уровне Windows. Запретил процессу Code.exe исходящие соединения в брандмауэре (правило с -RemoteAddress Internet; локальные адреса не затрагиваются, Strata продолжает работать). Цена: перестали работать магазин расширений, обновления и синхронизация в самом VS Code.

Общий вывод: приватность проверяется отдельно для каждого звена цепочки (модель, клиент, расширения, утилиты), а не один раз для модели. Расширение, которое “просто показывает окно чата”, по умолчанию может отправлять статистику, и это стоит проверять в настройках и в коде до первой рабочей задачи.

8. V100 не взлетает: не всякая плата выделяет ей окно памяти

У Tesla V100 32 ГБ большой BAR (область адресов, через которую видеопамять отображается для остальной системы), и чтобы карта вообще появилась в системе, материнская плата должна выделить под неё достаточно большое окно (в BIOS это обычно пункты “Above 4G Decoding” и Resizable BAR). Если окна не хватает, Windows ведёт себя так, будто видеокарты нет: не вижу её в диспетчере устройств или вижу с ошибкой, хотя физически карта исправна. Я наблюдал это на двух платах: на старой A320 V100 поднялась без возни, а на новой B550 под Windows не взлетела вообще. Логика “плата новее — значит лучше” тут не работает: дело не в возрасте платы, а в том, как конкретная прошивка UEFI считает и выделяет это окно, а свежая прошивка или её обновление не гарантированно это чинят — у меня обновление BIOS не помогло.

Linux в этом смысле устойчивее: ядро само пересчитывает и выделяет окна под BAR устройств независимо от того, что для этого зарезервировал BIOS, и той же платы и карты, которая отказывалась работать под Windows, Linux’у хватило без танцев с бубном. Отсюда и вывод для стенда: идеальная связка — это сервер с локальным ИИ на Linux, а управляешь им удалённо, по SSH или через свой канал связи (у меня для этого есть x_connector, см. раздел “Стенд”), а не напрямую с Windows на той же машине.

9. Понижать “уровень размышления” ради скорости — не всегда помогает

У Cline (и у самого Strata) есть параметр reasoning_effort (high/medium/low): логично было предположить, что на low модель думает меньше и отвечает быстрее. На простом вопросе (арифметическая задачка в одно действие) так и оказалось: low дал короче рассуждение и заметно быстрее ответ. Но это не перенеслось на задачу ближе к реальной работе: я дал модели кусок кода с классической ловушкой (var в цикле и замыкания в JS, где поверхностное чтение кода даёт неверный ответ, а не угадывание) и прогнал на high, medium и low. Все три уровня ответили одинаково правильно и разобрали механику корректно, а время (30,2–31,7 с) и число токенов в ответе (973–1074) совпали в пределах шума замера. Короче оказался только внутренний блок “рассуждения” (912 → 593 символов на low), но это никак не отразилось на итоговом времени: похоже, модель просто переносит часть разбора прямо в текст ответа вместо отдельного блока размышления.

Вывод: эффект от понижения уровня размышления зависит от типа задачи. На коротких фактических вопросах (посчитать, вспомнить факт) экономия реальная. На задачах, которые требуют развёрнутого ответа с объяснением, — а это как раз диагностика кода и правки по месту, то есть большая часть повседневной работы агента, — эффекта почти нет. Прежде чем менять настройку ради скорости, стоит проверить на задаче, близкой к своей, а не на игрушечном примере: результат может не повториться.

Локальные модели в реальных проектах (без цифр заказчика)

Восстановление данных 1С: architect и editor

В проекте по восстановлению данных из повреждённого файла базы локальный агент работал как “воркер”: исследования и скрипты, без права трогать информационную базу. Роли я разделил так:

  • editor: Qwen3-Coder (без рассуждения), механическая правка по готовому контракту;

  • architect: Gemma 4 (с рассуждением), диагноз и план.

Смешивать их в одном наборе правил вредно. Редактору нужен “атом”: правки в 1–3 файлах и проверяемая команда приёмки, а на задаче “разберись, в чём причина” он плохо достраивает недомолвки. Поэтому второй заход проекта я делал уже на модели с рассуждением.

Для делегирования я использовал стандартный пакет задачи: каталог запуска, цель, список файлов на чтение, белый список файлов на правку, шаги, команда приёмки, формат ответа и маркер BLOCKED вместо догадок. Принцип “подсказки советуют, проверки решают”: я верю результату команды приёмки, а не самоотчёту “готово”.

Грабли проекта, которые пригодятся всем:

  • самоотчёт модели не факт: на вопрос про рабочий каталог она ответила неверно, каталог надо проверять командой pwd;

  • оболочка у Qwen Code POSIX, а не PowerShell: Get-Location падает, лучше сразу дать pwd;

  • свежая сессия не помнит прошлых: в задаче нельзя писать “как в прошлый раз”, только явно;

  • “правильная причина” не равна “правильному выводу”: вердикт агента нужно перепроверять способом, который не повторяет метод поиска.

Whisper на CPU: распознавание речи для разбора сценария

В другом проекте (1С БП и интеграция с учётной системой) основную работу вели облачные агенты, а локальная модель понадобилась в одном месте: разобрать 6-минутный ролик “как раньше работал функционал” (экран 1С и голосовые пояснения). Облачный агент видео не воспринимает, поэтому звук я обработал локально, а кадры вынул ffmpeg.

Грабли, по порядку:

  1. av несовместим с faster-whisper (TypeError: unexpected keyword argument 'metadata_errors'). Обход: заранее вынуть звук ffmpeg-ом в сырой PCM 16 кГц и передавать модели массив.

  2. “CUDA видна” не значит “CUDA работает”: на Pascal (GTX 1070) нет быстрого FP16, а в pip-колёсах нет cublas64_12.dll. Итог: CPU, compute_type="int8".

  3. Hugging Face на Windows без режима разработчика предупреждает про симлинки и анонимные запросы: не критично, глушится переменной окружения.

  4. Первая загрузка модели 172 секунды, дальше из кэша около 5.

Где ещё: 1С, криптография и криптовалюты

Кроме двух разобранных выше случаев, локальная модель нужна мне везде, где в работе участвуют данные, которые не должны покидать машину:

  • Проектирование, разработка и тестирование функционала на 1С. Данные, с которыми я при этом работаю, никуда не должны уходить, поэтому код, запросы и разбор данных я стараюсь держать рядом с ними, на локальной модели.

  • Проекты по криптографии и криптовалюте. Тоже чувствительные данные: отправлять их во внешний сервис нельзя по самой природе задачи.

Подробностей по этим проектам (названия, данные, результаты) я не привожу: именно потому, что данные чувствительные. Принцип тот же: границу приватности проводят отдельно для каждого типа данных: что-то можно обсуждать с облачной моделью (общие вопросы, код без данных), а что-то нет. Цена этого решения честная: локальная модель слабее облачной, поэтому там, где ограничений нет, основную работу по-прежнему ведут облачные агенты.

Ощущения от работы и общая база знаний

По ощущениям локальный ИИ в работе примерно в 3–4 раза медленнее передовых облачных (это субъективная оценка, а не замер). Причина, как я её вижу, не в качестве кода, а в том, что облачные агенты сразу идут более оптимальными путями и берут более подходящие инструменты, а локальному агенту приходится наступать на грабли самому (пара примеров выше: оболочка, каталог запуска, пропущенные детали формата).

Надеюсь, что со временем отставание сократится, и работаю над этим так: набиваю локальную базу знаний в Obsidian, которой пользуются все мои агенты, и они записывают туда каждые грабли, на которые наступили, вместе с путём решения. Следующий агент (локальный или облачный) читает заметку и больше на эти грабли не наступает. Эффект этой практики я пока не измерял, это скорее ставка на накопление опыта.

Что дальше

  • Strata и 96 ГБ оперативки: замеры сделаны (см. раздел выше): 55–61 ток/с генерации на запросах до 239K токенов и до 66 ток/с с размышлением, чтение запроса около 1 200 ток/с; YaRN 512K и 1M на 96 ГБ тоже перемерены (там же). Дальше: оценить качество разных квантов (я мерил только скорость), попробовать вторую V100 на одном адаптере для большего кэша экспертов. Для серверов IBM AC922 есть отдельный экспериментальный форк Strata-AC922: на четырёх V100 по 16 ГБ автор получил генерацию в среднем 83 ток/с (пики до 113 на JSON, около 84 на прозе) и чтение запроса 1 967–6 353 ток/с на промптах 2K–74K и до 7 357 ток/с на 135K. Это замеры самого автора форка, независимых проверок он сам не нашёл, и я их не повторял.

  • Железо. Мой путь: сначала промежуточная сборка b550 - 1 (вторая V100 на переходнике), а дальше две ветки: самосборная платформа X99 с платой на четыре V100 с NVLink, или готовый сервер IBM AC922 (Power9, до шести V100). Стоимость вариантов и то, какие модели на какой помещаются, в подразделе ниже.

  • Цены на память. Серверная DDR5 RDIMM 32 ГБ в российской рознице на октябрь 2026 года стоит 55–180 тыс. рублей за модуль, DDR4 32 ГБ 18–38 тыс.; цифры из поиска по магазинам, не из Авито. Моя покупка: б/у на Авито пара 2×32 ГБ DDR4 за 30 тыс. рублей (15 тыс. за планку). Оперативка стала главной статьёй расходов любой такой сборки.

Куда двигаться дальше: две ветки апгрейда

Сейчас у меня стенд на плате b550 из этой статьи: одна V100 32 ГБ и 96 ГБ оперативки. Он обошёлся в 80 тр (V100 50 тр и память 30 тр) сверх домашнего ПК, который уже был. Дальше я рассматриваю такой путь (схема ниже). Сначала промежуточная сборка b550 - 1: ставлю переходник на два места SXM2 с NVLink и вторую V100 32 ГБ. От неё расходятся две ветки. Ветка X99: самосборная двухсокетная платформа (сборки x99 - 0 и x99 - 1). Ветка IBM AC922: готовый сервер на Power9 с шестью V100 (сборки IBM AC922 - 0 … IBM AC922 - 4). Я прикинул стоимость и то, какие крупные открытые MoE-модели (Qwen3.8-Max, Kimi K3, GLM-5.x, DeepSeek V4, MiniMax M3 и другие) на каждой поместятся.

Цены мои, б/у, на октябрь 2026 года. Для пути через X99: платформа X99 (двухсокетная) 15 тр, переходник на 2 места SXM2 с NVLink 30 тр, плата на 4 места SXM2 с NVLink 70 тр, V100 32 ГБ 65 тр, 256 ГБ памяти 140 тр. В x99 - 0 я переношу переходник с двумя V100 из b550 - 1, в x99 - 1 покупаю плату на 4 места и докупаю две V100 до комплекта. Бюджетный аналог платы на 4 места SXM2 — готовая плата Fujitsu с четырьмя V100 по 16 ГБ и NVLink (75 тр за всё вместе), но и ей нужна платформа X99. Для ветки AC922: шасси с шестью V100 16 ГБ и процессорами без памяти 200 тр, охлаждение 25 тр; память 256 ГБ — 140 тр, 512 ГБ — 240 тр, 1 ТБ — 384 тр, 2 ТБ — 1 040 тр; V100 32 ГБ 65 тр, свою V100 16 ГБ можно продать за 11 тр (доплата за замену 54 тр).

Две ветки апгрейда от стенда на b550: самосборная X99 и готовый IBM AC922; цена, общий объём памяти и модели, которые помещаются
Две ветки апгрейда от стенда на b550: самосборная X99 и готовый IBM AC922; цена, общий объём памяти и модели, которые помещаются

Схема к двум таблицам ниже: слева мой текущий стенд b550 и под ним промежуточная сборка b550 - 1, справа две ветки: сверху X99, снизу IBM AC922; на карточках цена в тр и то, что помещается.

В чём смысл этой развилки. X99 — гражданское железо, простое в сборке: берёшь обычную двухсокетную плату, переходник на SXM2 и карты. Но у неё быстро упирается потолок: ОЗУ и число карт ограничены, и по таблице ниже видно, что высшая лига локальных моделей (Kimi K3, крупный Qwen3.8-Max) ей недоступна ни в каком кванте. IBM AC922 сложнее в сборке и настройке (это уже серверная платформа Power9, не x86, со своими граблями), зато закрывает вопрос с домашним ИИ на ближайшие 5–7 лет: 2 ТБ ОЗУ и шесть V100 дают запас на поколения моделей вперёд. У AC922 есть и практическое преимущество, которого нет ни у X99, ни тем более у готовых станций вроде DGX Spark: на неё проще встраивать собственные модели в свои проекты и на ней можно дообучать свои модели — на DGX Spark с его 128 ГБ единой памяти это недостижимо. Дело не только в объёме памяти, а в архитектуре: у AC922 процессоры Power9 соединены с видеокартами не обычным PCIe, а NVLink 2.0, причём когерентно — GPU напрямую читает системную оперативку как продолжение своей памяти, на скорости 100–150 ГБ/с на карту (у X99 это PCIe x16, около 16 ГБ/с, на порядок медленнее). Именно такая связка CPU↔GPU и нужна при дообучении: когда оптимизатор, градиенты и активации не помещаются в видеопамять и приходится выгружать их в оперативку (стандартный приём вроде DeepSpeed ZeRO-Offload), на AC922 это не бутылочное горлышко, а на X99 — упрётся в шину. DGX Spark тут вообще не вариант: это один ускоритель с общей на процессор и GPU памятью, а не несколько дискретных карт, параллельное обучение на нём не устроить. Софт для этого на AC922 уже есть не только в теории: студенческий проект IBM и UFCG (llm-pt-ibm, Бразилия) собрал под Power9 PyTorch 2.6 из исходников, Ollama и vLLM (в контейнере Podman), и HuggingFace TRL — библиотеку именно для дообучения. Честно: готовых замеров скорости именно дообучения на AC922 я не нашёл, только подтверждение, что стек ставится и работает, плюс чужие замеры инференса (Ollama 79,8 ток/с на Llama 3.1 8B, форк Strata 83–113 ток/с на Qwen3.8-Flash-Next) и официальные цифры NVLink от IBM.

Стоимость (тр):

Сборка

Что внутри

ОЗУ + VRAM

Весов до

Цена, тр

Сейчас b550

домашний ПК (уже был) + V100 32 ГБ (50) + 96 ГБ ОЗУ (30)

128 ГБ

~115 ГБ

80 (без ПК)

b550 - 1

текущий b550 (96 ГБ ОЗУ) + переходник 2×SXM2 NVLink (30) + ещё одна V100 32 ГБ (65): итого 2×V100 по 32 ГБ

160 ГБ

~145 ГБ

+95 к стенду

x99 - 0

X99 2P (15) + 256 ГБ ОЗУ (140); переходник с двумя V100 переношу из b550 - 1

320 ГБ

~285 ГБ

+155, итого 250

x99 - 1

плата 4×SXM2 NVLink (70) + две V100 32 ГБ до комплекта (130): итого 4×V100 по 32 ГБ, 256 ГБ ОЗУ

384 ГБ

~345 ГБ

+200, итого 450

IBM AC922 - 0

AC922, 6×V100 16 ГБ, 256 ГБ

352 ГБ

~315 ГБ

365 (сервер целиком)

IBM AC922 - 1

AC922, 6×V100 16 ГБ, 512 ГБ

608 ГБ

~545 ГБ

465

IBM AC922 - 2

AC922, 4×16 + 2×32 ГБ, 512 ГБ

640 ГБ

~575 ГБ

573

IBM AC922 - 3

AC922, 6×V100 32 ГБ, 1 ТБ

1 216 ГБ

~1 090 ГБ

933

IBM AC922 - 4

AC922, 6×V100 32 ГБ, 2 ТБ

2 240 ГБ

~2 000 ГБ

1 589

(для сравнения) NVIDIA DGX Spark

GB10, 128 ГБ единой памяти LPDDR5x (273 ГБ/с), NVMe 4 ТБ

128 ГБ

~115 ГБ

520

(для сравнения) 2× DGX Spark

соединены напрямую кабелем по встроенному порту ConnectX-7 (200 Гбит/с, без коммутатора)

256 ГБ

~200 ГБ

~1 040 + кабель

(для сравнения) 4× DGX Spark

те же станции, но уже нужен отдельный коммутатор на 4×400G (кластер из четырёх узлов)

512 ГБ

~400 ГБ

~2 080 + коммутатор, около 150 тр

Для сборок b550 - 1 и X99 цена нарастающим итогом от стенда, без охлаждения; если прибавить 80 тр самого стенда, всего вложено 175 тр в b550 - 1, 330 тр в x99 - 0 и 530 тр в x99 - 1; в x99 - 1 переходник на 2 места (30 тр) становится ненужным, в итоговые 450 он засчитан (420 за плату, карты и платформу + 30 за переходник). Цена сборок IBM AC922 — это сервер целиком, карты из b550 - 1 в неё не входят.

Какой максимальный квант каждой модели помещается (размеры в ГБ; правило: веса не больше ~90% суммы ОЗУ и видеопамяти, остальное идёт на систему и кэш контекста; ⚠ — впритык, ❌ — не помещается; AC922 - N означает IBM AC922 - N):

Модель

b550 - 1

x99 - 0

x99 - 1

AC922 - 0

AC922 - 1

AC922 - 2

AC922 - 3

AC922 - 4

Qwen3.8-Max (2,4T)

❌

❌

❌

❌

UD-IQ1_S 508

UD-IQ1_M 564 ⚠

UD-IQ3_XXS 956

UD-IQ4_XS 1 310

Kimi K3 (2,8T)

❌

❌

❌

❌

❌

UD-IQ1_S 594 ⚠

UD-Q2_K_XL 861

Q4_K_XL 1 510 / Q8_K_XL 1 560

DeepSeek V4 Pro (1,6T)*

❌

❌

❌

❌

IQ1_S ~465 / IQ2 ~510

IQ2 ~510

Q4_K_XL ~870 / Q8 ~910

Q8 ~910

Kimi K2.7 Code (1T)

❌

❌ (1 бит 310 на грани)

1 бит 310; 2 бита 325–350 ⚠

1 бит 310 ⚠

Q3 385–470, IQ4_XS 546 ⚠

Q4_K_XL ~600 ⚠

Q8 ~605

Q8 ~605

MiMo-V2.6-Pro (1,02T)*

❌

❌

❌

❌

Q3_K_M ~498

Q3_K_M ~498

Q6_K ~842 / Q8_0 ~1 020 ⚠

Q8_0 ~1 020

GLM-5.3 / GLM-5.2 (~750B)

❌

IQ2_M 239

3 бита 290–345

IQ2_M 239 (3 бита от 290 ⚠)

4 бита 372–475

4 бита, 5 бит 570 ⚠

8 бит ~810

BF16 1 510

MiniMax M3 (428B)

IQ1_M 128 ⚠

Q4_K_XL 265

5 бит ~325 (Q4_K_XL 265)

Q4_K_XL 265

8 бит ~460–470

8 бит

BF16 ~855

BF16 ~855

DeepSeek V4.1 Flash (284B)

IQ4_XS 137 ⚠ (IQ3_S 116)

Q8_K_XL 162

Q8_K_XL 162

Q8_K_XL 162

то же

то же

то же

то же

GLM-5.3 Flash (320B)

IQ3_XXS 120

Q5_K_XL 240 (Q6 292 ❌)

Q8_0 341 ⚠ (Q6 292)

Q5_K_XL 240 (Q6 292 ⚠)

Q8_0 341

Q8_0 341

BF16 642

BF16 642

* Размеры оценочные: у DeepSeek V4 Pro нет опубликованных GGUF, цифры получены масштабированием по Flash; у MiMo-V2.6-Pro взяты из калькулятора. Остальное из страниц Unsloth и Hugging Face (вторичные источники, я напрямую файлы не проверял).

Что из этого следует:

  • Без 512 ГБ оперативки (IBM AC922 - 1 и выше) крупные модели (Kimi K3, Qwen3.8-Max, DeepSeek V4 Pro, MiMo) не запускаются вообще; на сборках с 256 ГБ (x99 - 0, x99 - 1, IBM AC922 - 0) идут только модели до ~430B параметров.

  • b550 - 1 (+95 тр) самый дешёвый шаг, но при 96 ГБ оперативки общий объём всего 160 ГБ: помещаются DeepSeek V4.1 Flash, GLM-5.3 Flash и MiniMax M3 только в одном бите. Две карты по 32 ГБ тут нужны скорее для кэша экспертов и контекста, чем для более крупных моделей.

  • x99 - 0 (250 тр нарастающим итогом) переносит те же две карты на платформу с 256 ГБ и открывает GLM-5.x в 2 битах и MiniMax M3 в 4 битах; x99 - 1 (450 тр) добавляет GLM-5.x в 3 битах и Kimi K2.7 Code в 1–2 битах.

  • IBM AC922 - 2 мало отличается от IBM AC922 - 1 по моделям: две карты по 32 ГБ добавляют всего 32 ГБ ёмкости за 108 тр.

  • Самая удобная точка по соотношению цены и возможностей среди серверов — IBM AC922 - 3 (933 тр): Q8 у Kimi K2.7 Code и DeepSeek V4 Pro, 8 бит у GLM-5.x, Qwen3.8-Max в 3 битах и Kimi K3 в 2 битах. IBM AC922 - 4 нужна только ради Kimi K3 в 4–8 битах, Qwen3.8-Max в 4 битах и GLM-5.x в BF16.

  • Для сравнения: готовая станция NVIDIA DGX Spark (520 тр) по ёмкости (128 ГБ единой памяти) равна моему стенду на b550 и чуть меньше b550 - 1, а крупные модели на ней не помещаются. Память у неё быстрее, чем у X99 (273 ГБ/с против 120–150), но по скорости на моей Qwen3.8-Flash-Next прироста ожидать не стоит: стенд на b550 уже даёт 55–61 ток/с, а в обзорах Spark показывает 38–60 ток/с на gpt-oss-120b. Преимущество Spark в компактности и готовом софте NVIDIA, а не в ёмкости.

  • Честно про Spark: несколько станций не складывают память в одну, как NVLink внутри AC922. Две и четыре станции соединяются по сети через встроенный порт ConnectX-7 (у пары — напрямую кабелем, у четвёрки нужен ещё коммутатор), а модель делится между узлами программно (vLLM с тензорным параллелизмом, llama.cpp RPC и подобное). По факту доступного объёма для модели это даёт прирост, но с накладными расходами: в живых тестах на форумах NVIDIA модель на ~200 ГБ весов уже не влезала в кластер из двух станций по 128 ГБ (256 ГБ номинально) — не хватало места под кэш контекста, пришлось брать четыре узла. И это очень дорогая история: у меня 520 тр за одну станцию, две — уже около 1 040 тр плюс кабель, а четыре — около 2 080 тр плюс коммутатор (ещё ~150 тр). При этом скорость с числом станций растёт медленнее цены: по собственным цифрам NVIDIA, две станции дают максимум в 1,7 раза больше одной (не вдвое), а в независимых тестах на четырёх станциях модель на 397 млрд параметров выдавала лишь 17–42 ток/с. Для тех же денег это уже уровень IBM AC922 - 2…3 из таблицы выше, с заметно большей ёмкостью.

  • Однобитные кванты по данным Unsloth сохраняют лишь 71–81% точности; для кода надёжнее 2–3 бита и выше.

  • По моему расчёту (не по замеру) скорость генерации при экспертах на процессоре определяется пропускной способностью памяти и числом активных параметров, а не объёмом оперативки: IBM AC922 - 1 не быстрее IBM AC922 - 0 на моделях, помещающихся в обе, а сборка x99 - 1 на X99 с DDR4 медленнее IBM AC922 - 0 на 15–38%. Это стоит проверить замером на реальном железе.

Выводы

  1. Измеряйте токены, а не слова: в моём тесте русские слова давали около 2,2 токена на слово.

  2. Не верьте ollama ps и строке состояния клиента: сверяйте prompt_eval_count, это единственное честное число. Подозрительную цифру проверяйте на втором движке: у меня Ollama показала для одной модели скорость выше физического предела, а llama.cpp на том же файле дал правдоподобные 30 ток/с.

  3. На V100 лучше всего идут MoE-модели с малым числом активных параметров: 110–133 ток/с у Qwen3-30B-A3B против 30–34 ток/с у плотной Gemma 31B.

  4. 128K на V100 реальны, но надёжнее через llama.cpp с квантованным KV-кэшем (24,9 ГБ вместо 31 ГБ): ценой скорости (на длинном контексте вдвое медленнее Ollama), зато без молчаливой обрезки запроса. Если окно небольшое, движки по скорости почти равны.

  5. В рабочих проектах локальная модель решает прежде всего вопрос “где лежат данные”, а не скорость или цену. Границу приватности проводите отдельно для каждого типа данных.

  6. Вердикт агента надо перепроверять независимым способом, а “данных нет” оформлять как нормальный результат.

  7. Свои замеры лучше публиковать со скриптом: github.com/DmNep/llm-bench-v100.

  8. Для MoE-движка с хранением экспертов в оперативке объём ОЗУ определяет скорость: на моём сервере переход с 48 на 96 ГБ удвоил скорость чтения запроса и дал +31–87% к генерации. Проверяйте прогнозы замером: моя экстраполяция по малым значениям бюджета оказалась слишком осторожной.

  9. Отставание локального агента от облачного (по моим ощущениям, в 3–4 раза по скорости работы) в основном про пути и инструменты, а не про качество кода; общая база знаний, куда агенты записывают грабли и решения, нужна, чтобы это отставание сокращать.

  10. Проверяйте оперативку Memtest86+ с флешки (не меньше двух проходов): в Linux memtester покрывает не весь объём и может пропустить дефект, а сбойная планка ломает систему незаметно.

  11. Выбирая железо под крупные MoE-модели, считайте суммарный объём оперативной и видеопамяти: от него зависит, какие модели и в каком кванте вообще запустятся. Скорость при этом определяет пропускная способность памяти процессора (мой расчёт, не замер), а не её объём.

  12. Размер окна модели в клиенте (Cline) ставьте заметно ниже серверного: нужен запас под ответ и вывод инструментов, а сжатие контекста может “пропускаться” и не сработать вовсе. Следите за строкой Context compacted, а не за настройкой, и храните состояние задачи в заметке, чтобы перенести её в новую.

  13. Проверяйте не только модель, но и клиенты вокруг неё: телеметрия расширений включена по умолчанию, облачные провайдеры остаются в настройках после миграции. Выключайте и удаляйте ненужное до первой рабочей задачи.

Продолжение следует. В разделе “Где ещё” выше я нарочно убрал цифры и детали реальных кейсов (1С, криптография) — они заслуживают отдельного подробного разбора, и я его сделаю следующей статьёй. А через две-три недели, когда придёт переходник на два места SXM2 с NVLink, будет апдейт с реальными замерами на сборке b550 - 1 (вторая V100, 160 ГБ ОЗУ+VRAM) — проверю на практике то, что сейчас в статье только прогноз.

Источник

  • 07.07.26 18:00 robertalfred175

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  • 07.07.26 18:01 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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  • 19.07.26 04:10 Fraddy Pual

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  • 19.07.26 19:53 kimberlyhebertt6877

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  • 19.07.26 19:53 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] Call/Text Number: +1 (336) 390-6684

  • 19.07.26 19:54 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] Call/Text Number: +1 (336) 390-6684

  • 30.07.26 00:04 Ahmed

    A really cool analysis, thank you. I was especially struck by how strictly the order is defined: data processing and formatting come first, with color only at the very end. This really saves you from the typical mistake of "first a pretty palette, then we figure out what the chart is for." And the palette validator with OKLCH + colorblindness check is absolutely fantastic; you almost never see that in regular tools. By the way, while reading about rank-trajectory and the logarithmic scale, I immediately remembered how convenient it is to analyze dynamics on charts in ExpertOption—I've been trading there for quite some time now. When the data is well visualized, decisions are noticeably easier and more relaxed. I also liked the point about "no more than eight colors" and the dual-axis ban. Strict restrictions sometimes actually produce better results than complete freedom. I'll try this approach myself.

  • 30.07.26 17:27 wendytaylor015

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  • 30.07.26 17:27 wendytaylor015

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  • 31.07.26 16:21 rssllhrnsb

    I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Doris Ashley, who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina
TEXT : (tatianasorina06 at G.Ma IL ..c 0 m ) Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.

  • 01.08.26 15:05 keithwilson9899

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  • 01.08.26 15:05 keithwilson9899

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  • 03.08.26 20:05 Philip

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  • 03.08.26 20:06 Philip

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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.

  • 04.08.26 12:37 kimberlyhebertt6877

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  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

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  • 11.09.26 03:23 kimberlyhebertt6877

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  • 06.10.26 17:44 Darrell1717

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  • 11.10.26 03:04 Patricia014

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  • 09.11.26 10:14 lydiasmitth456

    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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