mirror of
https://github.com/dogkeeper886/ollama37.git
synced 2025-12-09 23:37:06 +00:00
ef14fb5b26
This commit represents a complete rework after pulling the latest changes from official ollama/ollama repository and re-applying Tesla K80 compatibility patches. ## Key Changes ### CUDA Compute Capability 3.7 Support (Tesla K80) - Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt - Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset - Using 37-virtual (PTX with JIT compilation) for maximum compatibility ### Legacy Toolchain Compatibility - **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80) - **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7) - **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h) ### CPU Architecture Trade-offs Due to GCC 10.5 limitation, sacrificed newer CPU optimizations: - Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+) - Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA - Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility) ### Build System Updates - Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7 - Added -Wno-deprecated-gpu-targets flag to suppress warnings - Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI ### Upstream Sync Merged latest llama.cpp changes including: - Enhanced KV cache management with ISWA and hybrid memory support - Improved multi-modal support (mtmd framework) - New model architectures (Gemma3, Llama4, Qwen3, etc.) - GPU backend improvements for CUDA, Metal, and ROCm - Updated quantization support and GGUF format handling ### Documentation - Updated CLAUDE.md with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in official Ollama due to legacy driver/CUDA requirements. The toolchain constraint creates a deadlock: - K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI We accept the loss of cutting-edge CPU optimizations to enable running modern LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
82 lines
2.2 KiB
Plaintext
82 lines
2.2 KiB
Plaintext
## CPU only
|
|
|
|
```shell
|
|
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
|
|
```
|
|
|
|
## Nvidia GPU
|
|
|
|
Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation).
|
|
|
|
### Install with Apt
|
|
|
|
1. Configure the repository
|
|
|
|
```shell
|
|
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
|
|
| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
|
|
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
|
|
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
|
|
| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
|
|
sudo apt-get update
|
|
```
|
|
|
|
2. Install the NVIDIA Container Toolkit packages
|
|
|
|
```shell
|
|
sudo apt-get install -y nvidia-container-toolkit
|
|
```
|
|
|
|
### Install with Yum or Dnf
|
|
|
|
1. Configure the repository
|
|
|
|
```shell
|
|
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
|
|
| sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
|
|
```
|
|
|
|
2. Install the NVIDIA Container Toolkit packages
|
|
|
|
```shell
|
|
sudo yum install -y nvidia-container-toolkit
|
|
```
|
|
|
|
### Configure Docker to use Nvidia driver
|
|
|
|
```shell
|
|
sudo nvidia-ctk runtime configure --runtime=docker
|
|
sudo systemctl restart docker
|
|
```
|
|
|
|
### Start the container
|
|
|
|
```shell
|
|
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
|
|
```
|
|
|
|
<Note>
|
|
If you're running on an NVIDIA JetPack system, Ollama can't automatically discover the correct JetPack version.
|
|
Pass the environment variable `JETSON_JETPACK=5` or `JETSON_JETPACK=6` to the container to select version 5 or 6.
|
|
</Note>
|
|
|
|
## AMD GPU
|
|
|
|
To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
|
|
|
|
```shell
|
|
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
|
|
```
|
|
|
|
## Run model locally
|
|
|
|
Now you can run a model:
|
|
|
|
```shell
|
|
docker exec -it ollama ollama run llama3.2
|
|
```
|
|
|
|
## Try different models
|
|
|
|
More models can be found on the [Ollama library](https://ollama.com/library).
|