1. Fix binary path resolution using symlink (docker/runtime/Dockerfile)
- Build binary to source directory (./ollama)
- Create symlink from /usr/local/bin/ollama to /usr/local/src/ollama37/ollama
- Allows ml/path.go to resolve libraries via filepath.EvalSymlinks()
- Fixes "total vram=0 B" issue without requiring -w flag
2. Add comprehensive logging for model loading phases (llm/server.go)
- Log runner subprocess startup and readiness
- Log each memory allocation phase (FIT, ALLOC, COMMIT)
- Log layer allocation adjustments during convergence
- Log when model weights are being loaded (slowest phase)
- Log progress during waitUntilRunnerLaunched (every 1s)
- Improves visibility during 1-2 minute first-time model loads
3. Fix flash attention compute capability check (ml/device.go)
- Changed DriverMajor to ComputeMajor for correct capability detection
- Flash attention requires compute capability >= 7.0, not driver version
These changes improve user experience during model loading by providing
clear feedback at each stage, especially during the slow COMMIT phase
where GGUF weights are loaded and CUDA kernels compile.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Redesigned the Docker build system from a single-stage monolithic design to a clean
two-stage architecture that separates build environment from compilation process while
maintaining library path compatibility.
## Architecture Changes
### Builder Image (docker/builder/Dockerfile)
- Provides base environment: CUDA 11.4, GCC 10, CMake 4, Go 1.25.3
- Built once, cached for subsequent builds (~90 min first time)
- Removed config file copying (cuda-11.4.sh, gcc-10.conf, go.sh)
- Added comprehensive comments explaining each build step
- Added git installation for runtime stage source cloning
### Runtime Image (docker/runtime/Dockerfile)
- Two-stage build using ollama37-builder as base for BOTH stages
- Stage 1 (compile): Clone source from GitHub → CMake configure → Build C/C++/CUDA → Build Go
- Stage 2 (runtime): Copy artifacts from stage 1 → Setup environment → Configure server
- Both stages use identical base image to ensure library path compatibility
- Removed -buildvcs=false flag (VCS info embedded from git clone)
- Comprehensive comments documenting library paths and design rationale
### Makefile (docker/Makefile)
- Simplified from 289 to 145 lines (-50% complexity)
- Removed: run, stop, logs, shell, test targets (use docker-compose instead)
- Removed: build orchestration targets (start-builder, copy-source, run-cmake, etc.)
- Removed: artifact copying (handled internally by multi-stage build)
- Focus: Build images only (build, build-builder, build-runtime, clean, help)
- All runtime operations delegated to docker-compose.yml
### Documentation (docker/README.md)
- Completely rewritten for new two-stage architecture
- Added "Build System Components" section with file structure
- Documented why both runtime stages use builder base (library path compatibility)
- Updated build commands to use Makefile
- Updated runtime commands to use docker-compose
- Added comprehensive troubleshooting section
- Added build time and image size tables
- Reference to archived single-stage design
## Key Design Decision
**Problem**: Compiled binaries have hardcoded library paths
**Solution**: Use ollama37-builder as base for BOTH compile and runtime stages
**Trade-off**: Larger image (~18GB) vs guaranteed library compatibility
## Benefits
- ✅ Cleaner separation of concerns (builder env vs compilation vs runtime)
- ✅ Builder image cached after first build (90 min → <1 min rebuilds)
- ✅ Runtime rebuilds only take ~10 min (pulls latest code from GitHub)
- ✅ No library path mismatches (identical base images)
- ✅ No complex artifact extraction (multi-stage COPY)
- ✅ Simpler Makefile focused on image building
- ✅ Runtime management via docker-compose (industry standard)
## Files Changed
Modified:
- docker/builder/Dockerfile - Added comments, removed COPY config files
- docker/runtime/Dockerfile - Converted to two-stage build
- docker/Makefile - Simplified to focus on image building only
- docker/README.md - Comprehensive rewrite for new architecture
Deleted:
- docker/builder/README.md - No longer needed
- docker/builder/cuda-11.4.sh - Generated in Dockerfile
- docker/builder/gcc-10.conf - Generated in Dockerfile
- docker/builder/go.sh - Generated in Dockerfile
Archived:
- docker/Dockerfile → docker/Dockerfile.single-stage.archived
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add LD_LIBRARY_PATH to CMake and build steps for GCC 10 libraries
- Copy GCC 10 runtime libraries (libstdc++.so.6, libgcc_s.so.1) to output
- Update runtime Dockerfile to use minimal CUDA runtime packages
- Add -buildvcs=false flag to Go build to avoid Git VCS errors
- Simplify runtime container to only include necessary CUDA libraries
- Fix library path configuration for proper runtime library loading
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>
