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.

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

Quick Build

# Configure build (required on Linux/Intel macOS/Windows)
cmake -B build
cmake --build build

# For ROCm on Windows
cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
cmake --build build --config Release

# Build Go binary
go build -o ollama .

Tesla K80 Optimized Build

For Tesla K80 and CUDA Compute Capability 3.7 hardware, use specific compiler versions:

# 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/...

Docker

# Build standard image
docker build .

# Build with ROCm support
docker build --build-arg FLAVOR=rocm .

# Build ollama37 image for Tesla K80/Compute 3.7 support
docker build -f ollama37.Dockerfile -t ollama37 .

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, Gemma, 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