LLMIRDocumentation
LLMIR Documentation
Welcome to the LLMIR technical documentation. This section provides detailed information about the architecture, components, and features of the LLMIR compiler infrastructure.
Architecture Overview
LLMIR (Large Language Model Intermediate Representation) is a compiler infrastructure for large language models based on MLIR, designed to optimize and accelerate LLM inference through specialized compilation techniques.
Read more about LLMIR’s architecture →
Key Features
LLMIR includes several key features designed to optimize LLM inference:
KV Cache Optimization
Efficient management of key-value caches for transformer-based LLMs, including block-based allocation, optimized memory access patterns, and specialized attention operations.
Learn about KV Cache optimization →
Quantization Support
Comprehensive quantization capabilities for reducing model size and improving inference performance, including various quantization strategies and hardware-specific optimizations.
Explore quantization in LLMIR →
Distributed Deployment
Support for executing large models across multiple devices and nodes through tensor parallelism, pipeline parallelism, and efficient memory management.
Discover distributed deployment capabilities →
Performance Evaluation
Benchmarking and evaluation methodologies for measuring LLMIR’s impact on inference performance across different models and hardware platforms.
View performance evaluation approaches →
Development Status
LLMIR is currently in active development, following a phased approach:
- Phase 1 (Current Focus): Building the core infrastructure, including MLIR dialect design and implementation
- Phase 2 (Planned): Implementing core optimizations like KV cache management and attention fusion
- Phase 3 (Future): Adding advanced features such as quantization, parallelism strategies, and advanced hardware targeting
For more information on contributing to LLMIR, please see the Developer Guide.