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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
50 lines
1.6 KiB
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50 lines
1.6 KiB
Diff
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
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From: Julius Tischbein <ju.tischbein@gmail.com>
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Date: Wed, 15 Oct 2025 13:54:15 +0200
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Subject: [PATCH] CUDA: Changing the CUDA scheduling strategy to spin (#16585)
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MIME-Version: 1.0
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Content-Type: text/plain; charset=UTF-8
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Content-Transfer-Encoding: 8bit
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* CUDA set scheduling strategy to spinning for cc121
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* Using prop.major and prop.minor, include HIP and MUSA
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* Exclude HIP and MUSA
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* Remove trailing whitespace
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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* Remove empty line
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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---------
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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---
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ggml/src/ggml-cuda/ggml-cuda.cu | 9 +++++++++
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1 file changed, 9 insertions(+)
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diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
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index b075a18be..d62f412d6 100644
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--- a/ggml/src/ggml-cuda/ggml-cuda.cu
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+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
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@@ -340,6 +340,15 @@ static ggml_cuda_device_info ggml_cuda_init() {
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} else if (device_name.substr(0, 21) == "NVIDIA GeForce GTX 16") {
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turing_devices_without_mma.push_back({ id, device_name });
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}
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+
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+ // Temporary performance fix:
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+ // Setting device scheduling strategy for iGPUs with cc121 to "spinning" to avoid delays in cuda synchronize calls.
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+ // TODO: Check for future drivers the default scheduling strategy and
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+ // remove this call again when cudaDeviceScheduleSpin is default.
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+ if (prop.major == 12 && prop.minor == 1) {
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+ CUDA_CHECK(cudaSetDeviceFlags(cudaDeviceScheduleSpin));
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+ }
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+
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#endif // defined(GGML_USE_HIP)
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}
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