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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
54 lines
1.5 KiB
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54 lines
1.5 KiB
Plaintext
---
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title: n8n
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---
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## Install
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Install [n8n](https://docs.n8n.io/choose-n8n/).
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## Using Ollama Locally
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1. In the top right corner, click the dropdown and select **Create Credential**
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/n8n-credential-creation.png"
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alt="Create a n8n Credential"
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width="75%"
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/>
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</div>
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2. Under **Add new credential** select **Ollama**
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/n8n-ollama-form.png"
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alt="Select Ollama under Credential"
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width="75%"
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/>
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</div>
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3. Confirm Base URL is set to `http://localhost:11434` and click **Save**
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<Note> If connecting to `http://localhost:11434` fails, use `http://127.0.0.1:11434`</Note>
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4. When creating a new workflow, select **Add a first step** and select an **Ollama node**
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/n8n-chat-node.png"
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alt="Add a first step with Ollama node"
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width="75%"
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/>
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</div>
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5. Select your model of choice (e.g. `qwen3-coder`)
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<div style={{ display: 'flex', justifyContent: 'center' }}>
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<img
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src="/images/n8n-models.png"
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alt="Set up Ollama credentials"
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width="75%"
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/>
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</div>
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## Connecting to ollama.com
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1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**.
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2. In n8n, click **Create Credential** and select **Ollama**
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4. Set the **API URL** to `https://ollama.com`
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5. Enter your **API Key** and click **Save**
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