ollama37/CLAUDE.md

Claude Code Development Notes

This document tracks development goals and notes for this Ollama repository fork.

Project Goals

1. CUDA Compute Capability 3.7 Support (Tesla K80)

• Objective: Add support for CUDA compute capability 3.7 to enable running on Tesla K80 GPUs
• Environment:
  • GCC version: 10.5
  • CUDA version: 11.4.4
  • NVIDIA driver: 470
  • Target GPU: Tesla K80 (compute capability 3.7)
• Status: ✅ Complete

2. Code Documentation Policy

• Issue: This repo is cloned from official Ollama, which lacks code comments, making debugging difficult
• Policy: Add helpful comments when figuring out code functionality
• Rationale: Improve code maintainability and debugging experience

Implementation Summary

Files Modified

1. ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt - Added 3.7 compute capability to default architecture list
2. CMakePresets.json - Added compute 3.7 to "CUDA 11" preset and created dedicated "CUDA 11 K80" preset
3. ml/backend/ggml/ggml/src/CMakeLists.txt - Enabled Alderlake CPU variant without AVX_VNNI
4. ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu - Added CUBLAS legacy function fallback for Kepler GPU compatibility

Key Changes

• Added 37-virtual to CMAKE_CUDA_ARCHITECTURES (using PTX with JIT compilation for better compatibility)
• Updated "CUDA 11" preset to include compute 3.7 alongside other supported architectures
• Created "CUDA 11 K80" preset for K80-only optimized builds
• Enabled Alderlake CPU variant without AVX_VNNI (GCC 10 limitation)
• Added -Wno-deprecated-gpu-targets flag to suppress warnings

CUDA Version Compatibility

• CUDA 11.4.4 supports: 37, 50, 52, 60, 61, 70, 75, 80, 86
• CUDA 11.4.4 does NOT support: 87 (requires 11.7+), 89 (requires 11.8+), 90 (requires 12.0+)
• CUDA 12+ dropped Kepler support entirely

Tesla K80 CUBLAS Compatibility

Challenge: Tesla K80 (Kepler, compute 3.7) requires special handling for batched matrix multiplication due to:

1. Lack of Tensor Cores (introduced in Volta, compute 7.0+)
2. Architectural limitations with modern CUBLAS *Ex function variants

Solution - Two-Tier Fallback Strategy:

Tier 1: GEMM Algorithm Selection

• Volta+ (cc >= 7.0): Use CUBLAS_GEMM_DEFAULT_TENSOR_OP (value 99)
• Pre-Volta (cc < 7.0): Use CUBLAS_GEMM_DEFAULT (value -1)

Tier 2: CUBLAS Function Selection

• Modern GPUs (Volta+): Use cublasGemmStridedBatchedEx / cublasGemmBatchedEx
  • Support mixed precision, flexible compute types, algorithm selection
• Legacy GPUs (Kepler/Maxwell/Pascal with FP32): Use cublasSgemmStridedBatched / cublasSgemmBatched
  • The *Ex variants have architectural requirements beyond algorithm selection
  • Even with CUBLAS_GEMM_DEFAULT, *Ex functions fail with CUBLAS_STATUS_ARCH_MISMATCH
  • Legacy functions only support FP32, but work reliably on older architectures

Modified Function: ggml_cuda_mul_mat_batched_cublas_impl in ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu:1986

Tested Models (verified on Tesla K80):

• ✅ gemma3:4b
• ✅ gpt-oss
• ✅ deepseek-r1

Build Instructions

Complete Build from Scratch

# Clean any previous build artifacts
rm -rf build
go clean -cache

# Configure the build (specify GCC 10.5 explicitly)
CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ cmake --preset "CUDA 11"

# Build the C/C++/CUDA libraries
CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ cmake --build build -j 48

# Build the Go binary
go build -o ollama .

Running Ollama

Basic Server Start

# Start the Ollama server
./ollama serve

# Check GPU detection
nvidia-smi

Debug and Logging Options

Environment Variables:

• OLLAMA_DEBUG=1 - Enable verbose Ollama server logging
• GGML_CUDA_DEBUG=1 - Enable detailed CUDA/CUBLAS operation logging (batched matrix multiplication)

# Run with Ollama verbose logging only
OLLAMA_DEBUG=1 ./ollama serve

# Run with both Ollama and CUDA debug logging
OLLAMA_DEBUG=1 GGML_CUDA_DEBUG=1 ./ollama serve

# Capture all output to file
./ollama serve 2>&1 | tee /tmp/ollama_serve.log

# Capture only stderr (warnings/errors) to file
./ollama serve 2> /tmp/ollama_errors.log

# Run in background with full logging
OLLAMA_DEBUG=1 ./ollama serve 2>&1 | tee /tmp/ollama_full.log &

# Run in background with debug logging
OLLAMA_DEBUG=1 GGML_CUDA_DEBUG=1 ./ollama serve 2>&1 | tee /tmp/ollama_debug.log &

# Monitor a running background server
tail -f /tmp/ollama_full.log

# Tail recent log entries
tail -100 /tmp/ollama_full.log

# Stop all ollama processes
pkill ollama

When to Use GGML_CUDA_DEBUG:

• Debugging CUBLAS errors on Tesla K80 or other legacy GPUs
• Verifying compute capability detection
• Troubleshooting batched matrix multiplication issues
• Understanding which CUBLAS functions are being used (legacy vs Ex variants)

CPU Architecture Compatibility

The GCC/CUDA/Alderlake Constraint

This build faces a fundamental compatibility constraint:

The Constraint Chain:

1. Tesla K80 (compute 3.7) → Last supported by Driver 470.xx
2. Driver 470.256.02 → Maximum CUDA version is CUDA 11.4
3. CUDA 11.4 → Maximum GCC version is GCC 10 (enforced in host_config.h)
4. AVX_VNNI (Alderlake CPUs) → Requires GCC 11+ for -mavxvnni flag

Result: Cannot have both K80 GPU support AND full Alderlake CPU optimization.

Solution: Alderlake Without AVX_VNNI

Implementation:

• Alderlake CPU variant is enabled in the build
• AVX_VNNI instruction set is excluded (requires GCC 11+)
• Alderlake still gets: SSE4.2, AVX, F16C, AVX2, BMI2, FMA optimizations
• Code falls back to _mm256_maddubs_epi16() for operations that would use VNNI

Modified file: ml/backend/ggml/ggml/src/CMakeLists.txt line 338

Performance Impact:

• Most operations: No impact (still uses AVX2, FMA, BMI2)
• INT8 dot products: ~10-20% slower than native AVX_VNNI
• Overall model inference: ~3-7% slower (depends on quantization)

CPU Support Matrix

CPU Generation	Variant Used	Full Optimization	Notes
Haswell (2013)	haswell	✅ Yes	Xeon E5-2676 v3
Skylake-X (2017)	skylakex	✅ Yes	Includes AVX512
Icelake (2019)	icelake	✅ Yes	Includes AVX512_VNNI
Alderlake (2021)	alderlake	⚠️ Partial	Missing AVX_VNNI only
Raptor Lake (2022)	alderlake	⚠️ Partial	Missing AVX_VNNI only