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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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>
Prior to performing attention, we need to permute query, key
and value. Currently we call Contiguous after each of these
permutations, which is correct but expensive. Avoiding the
3 calls to Contiguous increases performance by over 20%.
The permutations of query and key do not violate the continuity
rules for mulmat and the Contiguous call can be simply removed.
Value requires a different permutation and does require Contiguous.
However, we can use the copy into the cache as a way to perform this
without further overhead.
To support this and avoid unexpected tensor shapes that are seen by
models, we need tighter integration between attention, cache
and backend. Future optimization will also likely need this structure
- for example, flash attention has special padding requirements in
the cache and other backends may have their own needs.
This further contains the operations that go into attention so that
these and other optimizations can be handled transparently. Models
that have special requirements for attention can still implement
their own version of it.
There are two benefits to doing this:
- Provide a library function that models can use, reducing code for
each model implementation
- Enables a single place to drop in optimized implementations of
attention based on the backend or other factors. One is provided for
GGML.
On CUDA this improves token generation rate by about 3%. It does not
have a significant effect on Metal.
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
