{"id":24603604,"url":"https://github.com/bjornmelin/llm-gpu-optimization","last_synced_at":"2025-03-18T08:43:00.875Z","repository":{"id":273969833,"uuid":"921477872","full_name":"BjornMelin/llm-gpu-optimization","owner":"BjornMelin","description":"🚄 Advanced LLM optimization techniques using CUDA. 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Features efficient attention mechanisms, custom CUDA kernels for transformers, and memory-efficient training strategies.\n\n[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)\n\n## 📑 Table of Contents\n- [Features](#features)\n- [Project Structure](#project-structure)\n- [Prerequisites](#prerequisites)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Documentation](#documentation)\n  - [Optimizations](#optimizations)\n  - [Memory Management](#memory-management)\n  - [Benchmarks](#benchmarks)\n- [Contributing](#contributing)\n- [Versioning](#versioning)\n- [Authors](#authors)\n- [Citation](#citation)\n- [License](#license)\n- [Acknowledgments](#acknowledgments)\n\n## ✨ Features\n- Flash Attention implementation\n- Efficient KV-cache management\n- Custom CUDA kernels for attention\n- Memory-efficient transformer layers\n- Multi-GPU training optimization\n\n## 📁 Project Structure\n\n```mermaid\ngraph TD\n    A[llm-gpu-optimization] --\u003e B[kernels]\n    A --\u003e C[models]\n    A --\u003e D[training]\n    A --\u003e E[benchmarks]\n    B --\u003e F[attention]\n    B --\u003e G[memory]\n    C --\u003e H[transformer]\n    C --\u003e I[tokenizer]\n    D --\u003e J[distributed]\n    D --\u003e K[optimization]\n    E --\u003e L[profiling]\n    E --\u003e M[metrics]\n```\n\n\u003cdetails\u003e\n\u003csummary\u003eClick to expand full directory structure\u003c/summary\u003e\n\n```plaintext\nllm-gpu-optimization/\n├── kernels/           # CUDA kernel implementations\n│   ├── attention/    # Optimized attention mechanisms\n│   └── memory/      # Memory management utilities\n├── models/           # Model implementations\n│   ├── transformer/ # Transformer architecture\n│   └── tokenizer/   # Tokenization optimizations\n├── training/         # Training utilities\n│   ├── distributed/ # Multi-GPU training\n│   └── optimization/# Training optimizations\n├── benchmarks/       # Performance benchmarks\n└── README.md         # Documentation\n```\n\u003c/details\u003e\n\n## 🔧 Prerequisites\n- CUDA Toolkit 11.8+\n- NVIDIA GPU (Compute Capability 8.0+)\n- PyTorch 2.2+\n- 32GB+ GPU RAM recommended\n- NVLink (for multi-GPU setup)\n\n## 📦 Installation\n\n```bash\n# Clone repository\ngit clone https://github.com/BjornMelin/llm-gpu-optimization.git\ncd llm-gpu-optimization\n\n# Create environment\npython -m venv venv\nsource venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n\n# Build CUDA extensions\npython setup.py install\n```\n\n## 🚀 Quick Start\n\n```python\nfrom llm_gpu import models, optimizers\n\n# Initialize model with optimizations\nmodel = models.OptimizedTransformer(\n    attention_type='flash',\n    use_kv_cache=True\n)\n\n# Configure distributed training\ntrainer = optimizers.DistributedTrainer(\n    model,\n    memory_efficient=True,\n    gradient_checkpointing=True\n)\n\n# Train with optimizations\ntrainer.train(dataset, batch_size=32)\n```\n\n## 📚 Documentation\n\n### Optimizations\n\n| Technique | Description | Memory Savings | Speed Improvement |\n|-----------|-------------|----------------|-------------------|\n| Flash Attention | Efficient attention computation | 80% | 3x |\n| KV Cache | Optimized key-value storage | 60% | 2x |\n| Gradient Checkpointing | Memory-efficient training | 70% | 0.8x |\n\n### Memory Management\n- Dynamic memory allocation\n- Gradient accumulation\n- Activation checkpointing\n- Memory-efficient attention patterns\n\n### Benchmarks\nPerformance on different model sizes:\n\n| Model Size | Batch Size | GPU | Memory Usage | Training Time |\n|------------|------------|-----|--------------|---------------|\n| 7B | 32 | A100-80GB | 76GB | 0.8s/step |\n| 13B | 16 | A100-80GB | 71GB | 1.2s/step |\n| 70B | 8 | 8xA100 | 64GB/GPU | 2.5s/step |\n\n## 🤝 Contributing\n- [Contributing Guidelines](CONTRIBUTING.md)\n- [Code of Conduct](CODE_OF_CONDUCT.md)\n- [Development Guide](DEVELOPMENT.md)\n\n## 📌 Versioning\nWe use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/llm-gpu-optimization/tags).\n\n## ✍️ Authors\n**Bjorn Melin**\n- GitHub: [@BjornMelin](https://github.com/BjornMelin)\n- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)\n\n## 📝 Citation\n```bibtex\n@misc{melin2024llmgpuopt,\n  author = {Melin, Bjorn},\n  title = {LLM GPU Optimization: Advanced CUDA Optimization for Language Models},\n  year = {2024},\n  publisher = {GitHub},\n  url = {https://github.com/BjornMelin/llm-gpu-optimization}\n}\n```\n\n## 📄 License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n- Flash Attention paper authors\n- HuggingFace Transformers team\n- NVIDIA for CUDA toolkit and documentation\n\n---\nMade with 🚄 and ❤️ by Bjorn Melin\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fllm-gpu-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjornmelin%2Fllm-gpu-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fllm-gpu-optimization/lists"}