LLMIR

Version 0.1.0

Large Language Model IR Compiler Framework

Documentation

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 has completed Phases 1–6 of its development roadmap:

  1. Phase 1–2 ✅: Core infrastructure, MLIR dialect, KV cache management, attention fusion
  2. Phase 3 ✅: Quantization (INT8/INT4), tensor/pipeline parallelism, multi-GPU sharding
  3. Phase 4 ✅: Speculative decoding, prefix caching, adaptive block management
  4. Phase 5 ✅: Continuous batching, vLLM integration, comprehensive benchmarks
  5. Phase 6 ✅: Python bindings, model-specific optimizations (Llama, Mistral, Phi), profiling tools
  6. Phase 7 (Planned): HuggingFace integration, distributed training, Kubernetes support

Verification: C++ MLIR dialect builds with MLIR 18; 84/84 unit tests passed. See Publications for the ICCD 2025 paper.

For more information on contributing to LLMIR, please see the Developer Guide.

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