mirror of
https://github.com/dogkeeper886/ollama37.git
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ef14fb5b26
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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
55 lines
1.2 KiB
Go
55 lines
1.2 KiB
Go
package discover
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/*
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#cgo CFLAGS: -x objective-c
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#cgo LDFLAGS: -framework Foundation -framework CoreGraphics -framework Metal
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#include "gpu_info_darwin.h"
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*/
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import "C"
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import (
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"log/slog"
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"syscall"
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"github.com/ollama/ollama/format"
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)
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const (
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metalMinimumMemory = 512 * format.MebiByte
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)
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func GetCPUMem() (memInfo, error) {
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return memInfo{
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TotalMemory: uint64(C.getPhysicalMemory()),
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FreeMemory: uint64(C.getFreeMemory()),
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// FreeSwap omitted as Darwin uses dynamic paging
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}, nil
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}
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func GetCPUDetails() []CPU {
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query := "hw.perflevel0.physicalcpu"
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perfCores, err := syscall.SysctlUint32(query)
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if err != nil {
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slog.Warn("failed to discover physical CPU details", "query", query, "error", err)
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}
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query = "hw.perflevel1.physicalcpu"
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efficiencyCores, _ := syscall.SysctlUint32(query) // On x86 xeon this wont return data
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// Determine thread count
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query = "hw.logicalcpu"
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logicalCores, _ := syscall.SysctlUint32(query)
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return []CPU{
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{
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CoreCount: int(perfCores + efficiencyCores),
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EfficiencyCoreCount: int(efficiencyCores),
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ThreadCount: int(logicalCores),
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},
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}
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}
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func IsNUMA() bool {
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// numa support in ggml is linux only
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return false
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}
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