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VeloLLM v0.1.0 - Phase 1 MVP

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@arthjean arthjean released this 02 Dec 22:34
v0.1.0
a21bb11

Autopilot for Local LLM Inference - First public release!

VeloLLM automatically detects your hardware and optimizes Ollama configuration for maximum performance.

What's Included

🔍 Hardware Detection (velollm detect)

Automatically detects your system configuration:

  • GPU: NVIDIA (CUDA), AMD (ROCm), Apple Silicon, Intel
  • CPU: Model, cores, threads, frequency
  • Memory: Total, available, used RAM
  • Platform: OS and architecture
velollm detect

⚡ Ollama Optimization (velollm optimize)

Generates optimal Ollama configuration based on your hardware:

  • OLLAMA_NUM_PARALLEL: Concurrent requests (based on VRAM)
  • OLLAMA_NUM_GPU: GPU layer offloading
  • OLLAMA_NUM_BATCH: Batch size for prompt processing
  • OLLAMA_NUM_CTX: Context window size
  • OLLAMA_MAX_LOADED_MODELS: Model caching
  • OLLAMA_KEEP_ALIVE: Model retention time
# Preview recommendations
velollm optimize --dry-run

# Generate shell script
velollm optimize -o velollm-config.sh
source velollm-config.sh

📊 Benchmarking Suite (velollm benchmark)

Measure inference performance with standardized tests:

  • Tokens/s: Generation throughput
  • TTFT: Time to first token (latency)
  • Total time: End-to-end performance
velollm benchmark --model llama3.2:3b -o results.json

Quick Start

Installation

# Clone and build
git clone https://github.com/ArthurDEV44/velollm.git
cd velollm
cargo build --release

# Or install directly
cargo install --path velollm-cli

Usage

# 1. Check your hardware
velollm detect

# 2. Optimize Ollama
velollm optimize -o velollm-config.sh
source velollm-config.sh

# 3. Benchmark (requires Ollama running)
ollama serve &
velollm benchmark --model llama3.2:3b

Benchmark Results

Tested on RTX 4070 Ti SUPER + Ryzen 7 7800X3D:

Benchmark Tokens/s TTFT
Short completion (50 tok) 65.6 16ms
Medium completion (150 tok) 170.5 22ms
Code generation (200 tok) 175.4 22ms
Average 137.2 20ms

See BENCHMARKS.md for detailed methodology.

Project Status

Phase 1 MVP - Complete ✅

Task Status
Hardware detection ✅
Ollama configuration parser ✅
Hardware-based optimizer ✅
CLI commands (detect, optimize, benchmark) ✅
Benchmarking suite ✅
Speculative decoding wrapper (llama.cpp) ✅
Integration tests (39 tests) ✅
Documentation ✅

Coming in Phase 2

  • PagedAttention for memory optimization
  • Continuous batching for concurrent users
  • Multi-backend support (llama.cpp, LocalAI, vLLM)
  • CPU-GPU hybrid execution

Documentation

Requirements

  • Rust: 1.70+ (for building)
  • Ollama: For benchmarking and optimization target
  • OS: Linux, macOS, Windows (WSL2)

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE

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