LLM Infrastructure Research

Project Overview

This repository serves as a comprehensive collection of research, implementations, and benchmarks focused on Large Language Model (LLM) infrastructure technologies. It documents my ongoing exploration of state-of-the-art methods for optimizing the deployment, efficiency, and performance of large language models.

Purpose

The primary goals of this repository are to:

1. Document systematic research on emerging LLM infrastructure technologies
2. Implement and reproduce key algorithms from recent literature
3. Provide benchmarks and comparative analyses of different approaches
4. Create a knowledge base for LLM infrastructure optimization techniques
5. Share practical insights gained through hands-on implementation

Research Focus

This repository covers various aspects of LLM infrastructure, including but not limited to:

1. Inference optimization techniques
2. Model quantization approaches (INT4/INT8, AWQ, GPTQ, etc.)
3. KV cache optimization strategies
4. Attention mechanism optimizations
5. Distributed inference systems
6. Memory management techniques
7. Serving architecture patterns
8. Hardware-specific optimizations

Repository Structure

The repository is organized into several key sections:

• /research: Literature reviews, technology surveys, and trend analyses
• /implementations: Reproduced algorithms with documentation
• /benchmarks: Performance evaluations and comparisons
• /tools: Utility scripts and helper functions
• /resources: Curated lists of papers, articles, and external resources

Methodology

Each implementation follows a structured approach:

1. Analysis: Thorough review of the original paper/method
2. Implementation: Clean, well-documented code reproduction
3. Evaluation: Comprehensive benchmarking against baselines
4. Documentation: Detailed explanations of principles and findings

Current Status

This is an active research repository with regular updates as new technologies emerge and additional implementations are completed. See the Issues section for planned work and the Projects board for current progress.

Contributions

While this repository primarily documents personal research, thoughtful contributions are welcome. Please see the CONTRIBUTING.md file for guidelines on how to contribute.

License

This project is licensed under [LICENSE_TYPE] - see the LICENSE file for details.

Note: This repository is intended for research and educational purposes. Performance claims and implementations should be validated for production use.