add docker

.env

@@ -1,14 +1,14 @@
-MODEL_FILE=Qwen3.6-35B-A3B-MTP-MXFP4_MOE.gguf
+MODEL_FILE=Qwen3.5-9B-MTP-UD-Q4_K_XL.gguf
 # Для -1 в .env лучше кавычки — иначе часть парсеров .env ломается на ведущем минусе.
-N_GPU_LAYERS="99"
+N_GPU_LAYERS="-1"
 
 # Доп. ключи docker-compose (опционально; дефолты заданы в compose)
-CTX_SIZE=170000
+CTX_SIZE=65536
-THREADS=12
+THREADS=1
 CACHE_TYPE_K=q8_0
 CACHE_TYPE_V=q8_0
 
 # MoE (Qwen3.5-35B-A3B-Q4_K_M / MXFP4_MOE и т.д.): при OOM на GPU — CPU_MOE=1 или частично N_CPU_MOE=8
 # Для dense (Qwopus 27B, Qwen 9B) держите оба 0.
 CPU_MOE=0
-N_CPU_MOE=29
+N_CPU_MOE=0

docker-compose.yml

@@ -1,14 +1,22 @@
-# llama.cpp + CUDA в Docker (Qwen3.x / крупные GGUF на 12 ГБ VRAM: гибрид GPU+CPU).
+# llama-cpp-turboquant + CUDA в Docker (Qwen3.x / крупные GGUF на 12 ГБ VRAM: гибрид GPU+CPU).
-# Образ по умолчанию: server-cuda (CUDA 12 в контейнере) — совместим с большинством GPU и драйверов.
+# Образы собираются локально из клона TheTom/llama-cpp-turboquant (см. docker/).
-# Альтернатива: server-cuda13 — только если GPU/драйвер тянут CUDA 13; иначе ggml_cuda_init:
-#   «forward compatibility was attempted on non supported HW» → оставьте server-cuda.
-# Нужны: драйвер NVIDIA, NVIDIA Container Toolkit, runtime nvidia в Docker.
 #
-# Модель: ./models/ + MODEL_FILE в .env.
+#   CUDA 12 (дефолт) — RTX 30xx / драйвер CUDA 12.x
-# Запуск: docker compose up -d или docker-compose up -d
+#   CUDA 13 — Blackwell (RTX 50xx, sm_120), драйвер ≥ CUDA 13.1:
-# Podman (одна команда, те же флаги): ./podman-llama.sh
+#     LLAMA_IMAGE_TAG=server-cuda13 LLAMA_DOCKERFILE=../llama-space/docker/server-cuda13.Dockerfile \
+#       docker-compose build
+#
+# Источник: LLAMA_CPP_SRC (дефолт ../llama-cpp-turboquant).
+# dockerfile — путь относительно context (так требует docker-compose 1.x).
+#
+# Сборка: docker-compose build
+# Запуск: docker-compose up -d
+# Podman: ./podman-llama.sh
 # Проверка: curl http://localhost:8080/health
 #
+# Нужны: драйвер NVIDIA, NVIDIA Container Toolkit, runtime nvidia в Docker.
+# Модель: ./models/ + MODEL_FILE в .env.
+#
 # OOM: уменьшите CTX_SIZE (дефолт 50000), -ngl и/или cache types; при «каше» попробуйте bf16/f16 для KV (больше VRAM).
 # MoE: в .env CPU_MOE=1 при необходимости; число слоёв MoE на CPU — N_CPU_MOE (дефолт 99 в command); для dense оставьте CPU_MOE=0.
 # --mlock + Docker: поднимите memlock ниже; если предупреждение остаётся — проверьте default-ulimits в /etc/docker/daemon.json и лимиты пользователя на хосте.
@@ -18,7 +26,12 @@ version: "3.8"
 
 services:
   llama-server:
-    image: ghcr.io/ggml-org/llama.cpp:server-cuda
+    # image: ghcr.io/ggml-org/llama.cpp:server-cuda13
+    image: llama-turboquant:${LLAMA_IMAGE_TAG:-server-cuda12}
+    build:
+      context: ${LLAMA_CPP_SRC:-../llama-cpp-turboquant}
+      # Relative to context (sibling of llama-space). Override for CUDA 13 — см. шапку.
+      dockerfile: ${LLAMA_DOCKERFILE:-../llama-space/docker/server-cuda12.Dockerfile}
     container_name: llama-server
     ports:
       # Явный IPv4 на хосте (избегаем привязки только к [::] в части окружений).
@@ -48,7 +61,7 @@ services:
       - "-c"
       - "${CTX_SIZE:-50000}"
       - "-np"
-      - "1"
+      - "4"
       - "-fa"
       - "on"
       - "--cache-type-k"

docker/build.sh

@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+# Сборка образов llama-cpp-turboquant (CUDA 12 / CUDA 13).
+# Контекст — клон TheTom/llama-cpp-turboquant; Dockerfile'ы — в этом репозитории.
+#
+#   ./docker/build.sh          # CUDA 12 (RTX 30xx)
+#   ./docker/build.sh 13       # CUDA 13 / sm_120 (RTX 50xx)
+#   ./docker/build.sh 12 13    # оба
+#
+# Переменные: LLAMA_CPP_SRC, DOCKER (docker|podman)
+
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+SRC="${LLAMA_CPP_SRC:-$(cd "$ROOT/../llama-cpp-turboquant" && pwd)}"
+DOCKER="${DOCKER:-docker}"
+
+if [[ ! -f "$SRC/CMakeLists.txt" ]]; then
+  echo "llama.cpp source not found at: $SRC" >&2
+  echo "Set LLAMA_CPP_SRC to the TheTom/llama-cpp-turboquant checkout." >&2
+  exit 1
+fi
+
+build_one() {
+  local ver="$1"
+  local df tag
+  case "$ver" in
+    12)
+      df="$ROOT/docker/server-cuda12.Dockerfile"
+      tag="llama-turboquant:server-cuda12"
+      ;;
+    13)
+      df="$ROOT/docker/server-cuda13.Dockerfile"
+      tag="llama-turboquant:server-cuda13"
+      ;;
+    *)
+      echo "Unknown CUDA major: $ver (use 12 or 13)" >&2
+      exit 1
+      ;;
+  esac
+
+  echo "==> Building $tag"
+  echo "    Dockerfile: $df"
+  echo "    Context:    $SRC"
+  "$DOCKER" build -f "$df" -t "$tag" "$SRC"
+}
+
+if [[ $# -eq 0 ]]; then
+  set -- 12
+fi
+
+for ver in "$@"; do
+  build_one "$ver"
+done

docker/server-cuda12.Dockerfile

@@ -0,0 +1,90 @@
+# llama-cpp-turboquant → llama-server (CUDA 12).
+# Build context: root of https://github.com/TheTom/llama-cpp-turboquant
+#   docker build -f docker/server-cuda12.Dockerfile -t llama-turboquant:server-cuda12 ../llama-cpp-turboquant
+#
+# Для RTX 30xx (sm_86) и типичных драйверов с CUDA 12.x.
+# Архитектуры — дефолт CMake (включая 86-real); не трогаем, чтобы не ломать
+# CMAKE_CUDA_ARCHITECTURES со списком через «;» в shell RUN.
+
+ARG UBUNTU_VERSION=24.04
+ARG CUDA_VERSION=12.8.1
+ARG GCC_VERSION=14
+ARG BASE_CUDA_DEV_CONTAINER=docker.io/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_CUDA_RUN_CONTAINER=docker.io/nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+
+ARG GCC_VERSION
+ARG CUDA_DOCKER_ARCH=default
+
+RUN apt-get update && \
+    apt-get install -y gcc-${GCC_VERSION} g++-${GCC_VERSION} build-essential cmake python3 python3-pip git libssl-dev libgomp1
+
+ENV CC=gcc-${GCC_VERSION} CXX=g++-${GCC_VERSION} CUDAHOSTCXX=g++-${GCC_VERSION}
+
+WORKDIR /app
+
+COPY . .
+
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
+RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
+    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release -j"$(nproc)" --target llama-server
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/llama-server /app/full/
+
+FROM ${BASE_CUDA_RUN_CONTAINER} AS server
+
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama-cpp-turboquant" \
+      org.opencontainers.image.description="llama-server with TurboQuant KV (CUDA 12)" \
+      org.opencontainers.image.source="https://github.com/TheTom/llama-cpp-turboquant"
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/lib/ /app
+COPY --from=build /app/full/llama-server /app/
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]

docker/server-cuda13.Dockerfile

@@ -0,0 +1,90 @@
+# llama-cpp-turboquant → llama-server (CUDA 13).
+# Build context: root of https://github.com/TheTom/llama-cpp-turboquant
+#   docker build -f docker/server-cuda13.Dockerfile -t llama-turboquant:server-cuda13 ../llama-cpp-turboquant
+#
+# Для Blackwell (RTX 50xx, sm_120). Toolkit 13.1.x — под драйвер с nvidia-smi CUDA 13.1.
+# Только 120a-real: быстрее сборка, FP4 tensor cores; не для карт старше Blackwell.
+
+ARG UBUNTU_VERSION=24.04
+ARG CUDA_VERSION=13.1.2
+ARG GCC_VERSION=14
+ARG BASE_CUDA_DEV_CONTAINER=docker.io/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_CUDA_RUN_CONTAINER=docker.io/nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+
+ARG GCC_VERSION
+# 120 → CMake подменит на 120a; явно 120a-real под Blackwell (не forwards-compatible).
+ARG CUDA_DOCKER_ARCH=120a-real
+
+RUN apt-get update && \
+    apt-get install -y gcc-${GCC_VERSION} g++-${GCC_VERSION} build-essential cmake python3 python3-pip git libssl-dev libgomp1
+
+ENV CC=gcc-${GCC_VERSION} CXX=g++-${GCC_VERSION} CUDAHOSTCXX=g++-${GCC_VERSION}
+
+WORKDIR /app
+
+COPY . .
+
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
+RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
+    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release -j"$(nproc)" --target llama-server
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/llama-server /app/full/
+
+FROM ${BASE_CUDA_RUN_CONTAINER} AS server
+
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama-cpp-turboquant" \
+      org.opencontainers.image.description="llama-server with TurboQuant KV (CUDA 13 / sm_120)" \
+      org.opencontainers.image.source="https://github.com/TheTom/llama-cpp-turboquant"
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 curl \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/lib/ /app
+COPY --from=build /app/full/llama-server /app/
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]

local-llama.service

@@ -1,25 +0,0 @@
-[Unit]
-Description=llama-server (local-llama)
-After=network.target
-
-[Service]
-Type=simple
-User=bazzite
-Group=bazzite
-WorkingDirectory=/home/bazzite/llama-space
-EnvironmentFile=/home/bazzite/llama-space/.env
-ExecStart=/home/bazzite/llama-space/local-llama.sh
-Restart=on-failure
-RestartSec=5
-
-# CPU pinning: taskset -c 0-11
-CPUAffinity=0-11
-
-# ulimit -l unlimited → lock memory (mlockall / MADV_POPULATE_READ + mlock)
-LimitMEMLOCK=infinity
-
-# Не убивать OOM killer — модель критична
-OOMScoreAdjust=-900
-
-[Install]
-WantedBy=multi-user.target

local-llama.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-
-export PATH=/usr/local/cuda-13.3/bin${PATH:+:${PATH}}
-export LD_LIBRARY_PATH=/usr/local/cuda-13.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
-
-set -euo pipefail
-
-DIR="$(cd "$(dirname "$0")" && pwd)"
-
-/home/bazzite/Документы/llama-cpp-turboquant/build/bin/llama-server \
-  --models-preset "${DIR}/models.ini" \
-  --models-dir "${DIR}/models" \
-  -np 1

models.ini

@@ -1,24 +0,0 @@
-[DEFAULT]
-host = 0.0.0.0
-port = 8080
-flash-attn = on
-chat-template = jinja
-reasoning = on
-reasoning-budget = -1
-cache-type-k = turbo4
-cache-type-v = turbo3
-no-mmap = true
-mlock = true
-ctx-checkpoints = true
-cache-ram = -1
-batch-size = 2048
-ubatch-size = 2048
-
-[Qwen3.6-35B]
-model = models/Qwen3.6-35B-A3B-MTP-MXFP4_MOE.gguf
-n-gpu-layers = 99
-n-cpu-moe = 29
-ctx-size = 170000
-threads = 12
-spec-type = draft-mtp
-spec-draft-n-max = 2

podman-llama.sh

@@ -9,6 +9,8 @@
 # Переменные — как в compose / .env: PORT, MODEL_FILE, CPU_MOE, N_CPU_MOE, CTX_SIZE,
 #   CACHE_TYPE_K, CACHE_TYPE_V, THREADS; образ: IMAGE (по умолчанию server-cuda).
 # Порт на хосте: только IPv4 (BIND_HOST по умолчанию 0.0.0.0), иначе Podman часто вешает [::].
+# Монтирование models: по умолчанию :ro,z (SELinux — иначе часто Permission denied в rootless).
+#   Свой суффикс: PODMAN_MODELS_OPTS=z  или  PODMAN_MODELS_OPTS=Z  или пусто PODMAN_MODELS_OPTS=
 
 set -euo pipefail
 
@@ -30,8 +32,9 @@ fi
 : "${CACHE_TYPE_K:=q8_0}"
 : "${CACHE_TYPE_V:=turbo2}"
 : "${THREADS:=8}"
-: "${IMAGE:=ghcr.io/ggml-org/llama.cpp:server-cuda13}"
+: "${IMAGE:=ghcr.io/ggml-org/llama.cpp:server-cuda}"
 : "${BIND_HOST:=0.0.0.0}"
+: "${PODMAN_MODELS_OPTS:=z}"
 
 # Разбить по пробелам для podman (пусто = только CDI-устройство).
 # Пример: PODMAN_GPU_FLAGS='--gpus all'
@@ -42,10 +45,9 @@ podman run -d \
   --replace \
   --restart no \
   --shm-size 1g \
-  --cpuset-cpus="0-11" \
   --ulimit memlock=-1:-1 \
   -p "${BIND_HOST}:${PORT}:8080" \
-  -v "${ROOT}/models:/models:ro" \
+  -v "${ROOT}/models:/models:ro${PODMAN_MODELS_OPTS:+,${PODMAN_MODELS_OPTS}}" \
   -e NVIDIA_VISIBLE_DEVICES=all \
   -e "LLAMA_ARG_CPU_MOE=${CPU_MOE}" \
   "${PODMAN_GPU_FLAGS_ARR[@]}" \
@@ -53,9 +55,7 @@ podman run -d \
   -m "/models/${MODEL_FILE}" \
   --host 0.0.0.0 \
   --port 8080 \
-  -ngl "${N_GPU_LAYERS}" \
   --n-cpu-moe "${N_CPU_MOE}" \
-  --spec-type draft-mtp --spec-draft-n-max 3 \
   -c "${CTX_SIZE}" \
   -np 1 \
   -fa on \
@@ -64,7 +64,7 @@ podman run -d \
   --no-mmap \
   --mlock \
   --ctx-checkpoints 1 \
-  --cache-ram -1 \
+  --cache-ram 0 \
   --jinja \
   --reasoning on \
   --reasoning-budget -1 \