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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
200 lines
4.0 KiB
Plaintext
200 lines
4.0 KiB
Plaintext
---
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title: Linux
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---
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## Install
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To install Ollama, run the following command:
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```shell
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curl -fsSL https://ollama.com/install.sh | sh
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```
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## Manual install
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<Note>
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If you are upgrading from a prior version, you should remove the old libraries
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with `sudo rm -rf /usr/lib/ollama` first.
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</Note>
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Download and extract the package:
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```shell
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curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
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| sudo tar zx -C /usr
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```
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Start Ollama:
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```shell
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ollama serve
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```
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In another terminal, verify that Ollama is running:
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```shell
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ollama -v
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```
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### AMD GPU install
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If you have an AMD GPU, also download and extract the additional ROCm package:
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```shell
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curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
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| sudo tar zx -C /usr
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```
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### ARM64 install
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Download and extract the ARM64-specific package:
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```shell
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curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
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| sudo tar zx -C /usr
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```
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### Adding Ollama as a startup service (recommended)
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Create a user and group for Ollama:
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```shell
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sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama
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sudo usermod -a -G ollama $(whoami)
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```
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Create a service file in `/etc/systemd/system/ollama.service`:
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```ini
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[Unit]
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Description=Ollama Service
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After=network-online.target
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[Service]
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ExecStart=/usr/bin/ollama serve
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User=ollama
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Group=ollama
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Restart=always
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RestartSec=3
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Environment="PATH=$PATH"
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[Install]
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WantedBy=multi-user.target
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```
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Then start the service:
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```shell
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sudo systemctl daemon-reload
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sudo systemctl enable ollama
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```
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### Install CUDA drivers (optional)
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[Download and install](https://developer.nvidia.com/cuda-downloads) CUDA.
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Verify that the drivers are installed by running the following command, which should print details about your GPU:
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```shell
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nvidia-smi
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```
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### Install AMD ROCm drivers (optional)
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[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v6.
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### Start Ollama
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Start Ollama and verify it is running:
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```shell
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sudo systemctl start ollama
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sudo systemctl status ollama
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```
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<Note>
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While AMD has contributed the `amdgpu` driver upstream to the official linux
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kernel source, the version is older and may not support all ROCm features. We
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recommend you install the latest driver from
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https://www.amd.com/en/support/linux-drivers for best support of your Radeon
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GPU.
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</Note>
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## Customizing
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To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
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```shell
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sudo systemctl edit ollama
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```
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Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`:
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```ini
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[Service]
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Environment="OLLAMA_DEBUG=1"
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```
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## Updating
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Update Ollama by running the install script again:
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```shell
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curl -fsSL https://ollama.com/install.sh | sh
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```
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Or by re-downloading Ollama:
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```shell
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curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
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| sudo tar zx -C /usr
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```
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## Installing specific versions
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Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases).
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For example:
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```shell
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curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
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```
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## Viewing logs
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To view logs of Ollama running as a startup service, run:
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```shell
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journalctl -e -u ollama
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```
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## Uninstall
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Remove the ollama service:
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```shell
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sudo systemctl stop ollama
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sudo systemctl disable ollama
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sudo rm /etc/systemd/system/ollama.service
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```
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Remove ollama libraries from your lib directory (either `/usr/local/lib`, `/usr/lib`, or `/lib`):
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```shell
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sudo rm -r $(which ollama | tr 'bin' 'lib')
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```
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Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`):
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```shell
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sudo rm $(which ollama)
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```
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Remove the downloaded models and Ollama service user and group:
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```shell
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sudo userdel ollama
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sudo groupdel ollama
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sudo rm -r /usr/share/ollama
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```
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