ollama37/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Goal

This project (ollama37) exists to maintain support for NVIDIA Tesla K80 GPUs and other Compute Capability 3.7 hardware. The official Ollama release has deprecated support for these older GPUs, but this fork keeps them functional by:

• Maintaining sync with the official Ollama repository for latest features and fixes
• Preserving CUDA Compute Capability 3.7 support that was removed from upstream
• Providing a specialized build optimized for Tesla K80 and similar legacy hardware

This enables users with older NVIDIA GPUs to continue running modern LLMs locally without requiring hardware upgrades.

CUDA 3.7 Support Implementation

CUDA Compute Capability 3.7 support is maintained in the following key locations:

• ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt:7 - Core build configuration with CMAKE_CUDA_ARCHITECTURES "37;50;61;70;75;80"
• CMakePresets.json:24 - "CUDA 11" preset includes "37" (CUDA 12 dropped 3.7 support)
• README.md:63-66 - Tesla K80 support overview and technical details
• docs/manual-build.md - Comprehensive Tesla K80 build instructions and optimizations
• docs/gpu.md:33 - General GPU building guidance

The project uses CUDA 11 toolchain to maintain compatibility with Tesla K80 and other Compute Capability 3.7 GPUs, as CUDA 12 officially dropped support for these architectures.

CC 3.7-Only Optimization Strategy

Status: ✅ COMPLETED - All 9 phases complete and tested successfully

Completion Summary: Successfully simplified CUDA backend to support only CC 3.7 (Kepler/Tesla K80). After the initial optimization removed modern GPU architecture constants from common.cuh, additional fixes were required to handle undefined constant references throughout the codebase. All MMA (tensor core) functions have been properly disabled while preserving DP4A functions for CC 3.7 compatibility.

Critical Runtime Fix - Phase 9 (2025-10-29): After Phase 8, CUDA backend failed to load due to undefined Flash Attention symbols. Solution implemented:

1. Disabled all flash attention helper functions with #if 0 (lines 126-274 in fattn.cu)
2. Simplified main ggml_cuda_flash_attn_ext() function to abort immediately for CC 3.7
3. Added GGML_UNUSED macros to prevent compiler warnings
4. Build successful ✅
5. Runtime testing successful ✅ - CUDA backend loads, GPU offloading works correctly

Verified Working:

• ✅ CUDA backend loads without undefined symbol errors
• ✅ Log shows: load_backend: loaded CUDA backend from libggml-cuda.so
• ✅ Layers offload to GPU correctly (e.g., 35/35 layers for gemma3:4b)
• ✅ Fast GPU inference confirmed

Goal: Simplify the codebase by removing support for all CUDA Compute Capabilities except 3.7, since newer GPUs (CC 5.0+) are already supported by upstream Ollama.

Rationale

• Upstream Ollama: Supports CC 5.0+ (Maxwell and newer GPUs)
• Unique to Ollama37: Only CC 3.7 (Kepler - Tesla K80, K40, M40)
• Clear positioning: "For Tesla K80 and Kepler GPUs only"
• Benefits:
  • 80-85% smaller binaries (compile for 1 arch instead of 6)
  • 5-6x faster build times
  • Simpler codebase (no dead code for features CC 3.7 doesn't have)
  • Easier maintenance and debugging

Features NOT Available on CC 3.7

The following modern GPU features can be removed from the codebase:

• FP16 native operations (requires CC 6.0+ Pascal)
• DP4A instruction (int8 dot product, requires CC 6.1+)
• Tensor Cores / MMA / WMMA (requires CC 7.0+ Volta/Turing)
• Async memory copy / CP_ASYNC (requires CC 8.0+ Ampere)
• Flash Attention (requires CC 7.0+)
• Stream-K scheduling (optimized for CC 7.0+)

✅ Completed Optimizations

Files completely removed (~116KB):

• ✅ ml/backend/ggml/ggml/src/ggml-cuda/mma.cuh - Tensor core operations
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/fattn-wmma-f16.cu - Flash attention with WMMA
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/fattn-mma-f16.cuh
• ✅ 39 template instantiation files for MMA/WMMA operations

Files simplified for CC 3.7 only:

• ✅ ml/backend/ggml/ggml/src/ggml-cuda/common.cuh - Removed architecture detection, hardcoded CC 3.7
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/mmq.cuh - Disabled 6 MMA functions, removed Volta+ optimizations
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/mmq.cu - Disabled Stream-K scheduling
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/convert.cu - Disabled Pascal+ FP16 code block
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/mmv.cu - Hardcoded FP32 precision
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/mmvmxfp4.cu - Hardcoded FP32 precision
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu - Disabled BF16, CUDA graphs, Volta checks
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/fattn.cu - Replaced Ada Lovelace constant references
• ✅ ml/backend/ggml/ggml/src/ggml-cuda/fattn-common.cuh - Simplified Stream-K scheduling

Build configuration:

• ✅ ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt:7 - Set to "37" only
• ✅ CMakePresets.json:25 - CUDA 11 preset set to "37" only

Post-completion fixes (architecture constant references):

• ✅ Fixed undefined GGML_CUDA_CC_PASCAL, GGML_CUDA_CC_VOLTA, GGML_CUDA_CC_DP4A, GGML_CUDA_CC_ADA_LOVELACE in 4 files
• ✅ Corrected overly broad MMA disabling in mmq.cuh that accidentally disabled DP4A functions
• ✅ Set all vec_dot_mma function pointers to nullptr in mmq_type_traits structs

Implementation Tracking

Detailed cleanup instructions are maintained in folder-specific CLAUDE.md files:

• ml/backend/ggml/ggml/src/ggml-cuda/CLAUDE.md - CUDA kernel cleanup instructions
• ml/CLAUDE.md - Go-level GPU detection simplification
• llm/CLAUDE.md - Memory estimation optimization for single-GPU preference

These files contain specific line numbers, code blocks, and commands to execute the cleanup incrementally across sessions.

🎯 Tesla K80 Performance Optimizations

Memory Estimation Optimization for Single-GPU Preference

Status: ✅ COMPLETED - Fully implemented and tested (2025-10-30)

Goal: Eliminate unnecessary multi-GPU splits by fixing graph memory overestimation for Tesla K80.

Phase 1: Per-GPU Graph Allocation (2025-10-29)

Problem: Multi-GPU systems allocated full graph memory (1.3 GiB) to EACH GPU, causing 2.6 GiB total overestimation.

Solution: Secondary GPUs use 190 MiB, primary GPU uses full 1.3 GiB (based on empirical measurements).

Results: gemma3:12b split improved from 25,24 → 1,48 layers, but still not single-GPU.

Phase 2: CC 3.7 Graph Correction Factor (2025-10-30)

Problem: Graph estimates were 15-20% higher than actual usage for CC 3.7 GPUs:

• Estimated: 1.3 GiB
• Actual: 1.1 GiB
• This caused gemma3:12b single-GPU check to fail by ~200 MiB margin

Root Cause: Output layer (2.6 GiB) couldn't fit after 48 layers (8.5 GiB) due to overestimated graph overhead.

Solution (llm/memory.go:173-182):

// Apply empirical 85% correction factor for Tesla K80 (CC 3.7)
if gpus[0].Library == "cuda" && gpus[0].Compute == "3.7" {
    graphPartialOffload = (graphPartialOffload * 85) / 100
    graphFullOffload = (graphFullOffload * 85) / 100
}

Results Achieved:

• gemma3:4b: Single GPU ✅
• gemma3:12b: Single GPU ✅ (was 1,48 split)
• Memory estimate: 11.9 GiB → 11.0 GiB (-900 MiB)
• Actual usage: 10.0 GiB on single GPU
• GPU utilization: 94% during inference
• nvidia-smi: GPU 0: 10,015 MiB, GPU 1: 7 MiB (idle)

Technical Details:

• Only affects CUDA CC 3.7 GPUs (Tesla K80, K40, M40)
• No impact on newer GPUs (CC 5.0+)
• Maintains 10% safety margin between estimate and actual
• Preserves multi-GPU functionality for models >11 GiB

Benefits:

• ✅ gemma3:12b runs on single GPU (no cross-GPU communication)
• ✅ Faster inference (no tensor split overhead)
• ✅ Better VRAM utilization
• ✅ Empirically validated with real measurements
• ✅ Conservative correction maintains stability

Model Architecture Compatibility

GPT-OSS Model Fix (2025-10-29)

Issue: The gpt-oss model architecture code expected fused tensor formats that didn't match the actual GGUF file structure, causing nil pointer panics.

Root Cause: Mismatch between code expectations and GGUF file format:

• Code expected: attn_qkv (fused), ffn_gate_up_exps (fused)
• GGUF contains: attn_q/k/v (separate), ffn_gate_exps/up_exps (separate)

Fix Applied (model/models/gptoss/model.go):

1. Updated AttentionBlock struct to use separate Query, Key, Value fields instead of fused QKV
2. Modified AttentionBlock.Forward() to compute Q/K/V projections separately
3. Updated MLPBlock struct to use separate Gate and Up fields instead of fused GateUp
4. Modified MLPBlock.Forward() to compute gate/up separately and removed incorrect reshape

Result: ✅ gpt-oss:20b model now loads and runs successfully on Tesla K80

Documentation Structure

The project documentation is organized as follows:

• README.md - Concise overview, quick start, and basic usage (restructured for clarity)
• docs/manual-build.md - Comprehensive manual build instructions for Tesla K80 optimization
• docs/gpu.md - General GPU support and configuration
• docs/api.md - Complete REST API reference
• docs/development.md - Development setup and contribution guidelines
• CLAUDE.md - This file, providing AI assistant guidance for the codebase

Development Commands

Building the Project

# Configure with GCC 10 and CUDA 11.4 support
CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ cmake -B build
CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ cmake --build build

# Build Go binary
go build -o ollama .

For complete Tesla K80 build instructions including prerequisite installation, see docs/manual-build.md.

Running Ollama

# Run development server
go run . serve

# Start server with built binary
./ollama serve

Testing

# Run all tests
go test ./...

# Run tests with synctest (for Go 1.24 compatibility)
GOEXPERIMENT=synctest go test ./...

# Run integration tests (requires server running)
go test ./integration/...

# Run specific test package
go test ./server/...

Architecture Overview

Ollama is a local LLM server with Go backend and C++/CUDA acceleration:

Core Components

Entry Point: main.go uses Cobra CLI framework, delegating to cmd/ package for command handling.

Server Layer (server/): HTTP server built on Gin framework handling:

• REST API endpoints (routes.go)
• Model management (download, create, delete)
• Chat and generation endpoints
• Model scheduling and GPU resource management (sched.go)

LLM Integration (llm/): Abstracts language model backends with platform-specific implementations:

• server.go - LLM server process management
• memory.go - GPU memory management
• Platform-specific files for Darwin, Linux, Windows

Model Layer (model/): Handles model format conversion and tokenization:

• models/ - Model-specific implementations (Llama, Gemma3n, etc.)
• imageproc/ - Image processing for multimodal models
• Tokenizer implementations (BPE, SentencePiece)

ML Backend (ml/backend/ggml/): C++ acceleration layer built on GGML:

• CPU optimizations with SIMD
• CUDA GPU acceleration
• ROCm/HIP support for AMD GPUs
• Memory-mapped model loading

Conversion Pipeline (convert/): Converts models from HuggingFace/PyTorch formats to GGUF:

• Architecture-specific converters for different model families
• Safetensors and PyTorch tensor reading
• Quantization support

Key Data Flow

1. Model Loading: Models downloaded/converted to GGUF format, stored locally
2. Request Processing: HTTP requests parsed, routed through server layer
3. Model Scheduling: GPU resources allocated, models loaded into memory
4. Inference: Requests forwarded to appropriate LLM backend process
5. Response Streaming: Generated tokens streamed back via HTTP

GPU Acceleration

The project supports multiple acceleration backends:

• CUDA: NVIDIA GPU support via ml/backend/ggml/ggml/src/ggml-cuda/
• Metal: Apple Silicon native support
• ROCm/HIP: AMD GPU support
• CPU: Optimized CPU kernels with AVX/NEON

Libraries are dynamically loaded from:

• ./lib/ollama (Windows)
• ../lib/ollama (Linux)
• . (macOS)
• build/lib/ollama (development)

Configuration

• Environment variables prefixed with OLLAMA_ (envconfig/)
• Model templates in template/ directory
• Tool definitions in tools/ for function calling

Testing Structure

• Unit tests throughout codebase (*_test.go)
• Integration tests in integration/ requiring running server
• Benchmark tests for performance validation
• Platform-specific test files for GPU/hardware features