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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
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1.7 KiB
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64 lines
1.7 KiB
Plaintext
---
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title: Authentication
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---
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No authentication is required when accessing Ollama's API locally via `http://localhost:11434`.
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Authentication is required for the following:
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* Running cloud models via ollama.com
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* Publishing models
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* Downloading private models
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Ollama supports two authentication methods:
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* **Signing in**: sign in from your local installation, and Ollama will automatically take care of authenticating requests to ollama.com when running commands
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* **API keys**: API keys for programmatic access to ollama.com's API
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## Signing in
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To sign in to ollama.com from your local installation of Ollama, run:
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```
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ollama signin
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```
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Once signed in, Ollama will automatically authenticate commands as required:
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```
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ollama run gpt-oss:120b-cloud
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```
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Similarly, when accessing a local API endpoint that requires cloud access, Ollama will automatically authenticate the request:
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```shell
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curl http://localhost:11434/api/generate -d '{
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"model": "gpt-oss:120b-cloud",
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"prompt": "Why is the sky blue?"
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}'
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```
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## API keys
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For direct access to ollama.com's API served at `https://ollama.com/api`, authentication via API keys is required.
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First, create an [API key](https://ollama.com/settings/keys), then set the `OLLAMA_API_KEY` environment variable:
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```shell
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export OLLAMA_API_KEY=your_api_key
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```
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Then use the API key in the Authorization header:
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```shell
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curl https://ollama.com/api/generate \
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-H "Authorization: Bearer $OLLAMA_API_KEY" \
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-d '{
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"model": "gpt-oss:120b",
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"prompt": "Why is the sky blue?",
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"stream": false
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}'
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```
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API keys don't currently expire, however you can revoke them at any time in your [API keys settings](https://ollama.com/settings/keys).
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