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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
48 lines
1.3 KiB
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48 lines
1.3 KiB
Plaintext
---
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title: JetBrains
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---
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<Note>This example uses **IntelliJ**; same steps apply to other JetBrains IDEs (e.g., PyCharm).</Note>
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## Install
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Install [IntelliJ](https://www.jetbrains.com/idea/).
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## Usage with Ollama
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<Note>
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To use **Ollama**, you will need a [JetBrains AI Subscription](https://www.jetbrains.com/ai-ides/buy/?section=personal&billing=yearly).
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</Note>
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1. In Intellij, click the **chat icon** located in the right sidebar
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/intellij-chat-sidebar.png"
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alt="Intellij Sidebar Chat"
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width="50%"
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/>
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</div>
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2. Select the **current model** in the sidebar, then click **Set up Local Models**
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/intellij-current-model.png"
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alt="Intellij model bottom right corner"
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width="50%"
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/>
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</div>
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3. Under **Third Party AI Providers**, choose **Ollama**
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4. Confirm the **Host URL** is `http://localhost:11434`, then click **Ok**
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5. Once connected, select a model under **Local models by Ollama**
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/intellij-local-models.png"
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alt="Zed star icon in bottom right corner"
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width="50%"
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/>
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</div>
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