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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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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59 lines
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---
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title: Ollama's documentation
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sidebarTitle: Welcome
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---
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<img src="/images/welcome.png" noZoom className="rounded-3xl" />
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[Ollama](https://ollama.com) is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more.
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<CardGroup cols={2}>
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<Card title="Quickstart" icon="rocket" href="/quickstart">
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Get up and running with your first model
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</Card>
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<Card
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title="Download Ollama"
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icon="download"
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href="https://ollama.com/download"
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>
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Download Ollama on macOS, Windows or Linux
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</Card>
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<Card title="Cloud" icon="cloud" href="/cloud">
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Ollama's cloud models offer larger models with better performance.
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</Card>
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<Card title="API reference" icon="terminal" href="/api">
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View Ollama's API reference
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</Card>
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</CardGroup>
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## Libraries
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<CardGroup cols={2}>
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<Card
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title="Ollama's Python Library"
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icon="python"
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href="https://github.com/ollama/ollama-python"
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>
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The official library for using Ollama with Python
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</Card>
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<Card title="Ollama's JavaScript library" icon="js" href="https://github.com/ollama/ollama-js">
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The official library for using Ollama with JavaScript or TypeScript.
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</Card>
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<Card title="Community libraries" icon="github" href="https://github.com/ollama/ollama?tab=readme-ov-file#libraries-1">
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View a list of 20+ community-supported libraries for Ollama
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</Card>
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</CardGroup>
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## Community
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<CardGroup cols={2}>
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<Card title="Discord" icon="discord" href="https://discord.gg/ollama">
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Join our Discord community
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</Card>
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<Card title="Reddit" icon="reddit" href="https://reddit.com/r/ollama">
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Join our Reddit community
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</Card>
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</CardGroup>
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