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Why use LLM Compressor?
Compresssing your model, step-by-step
Compresssing your model, step-by-step
Choosing your model
Choosing the right compression scheme
Choosing the right compression algorithm
Choosing a dataset
Compressing your model
Deploying with vLLM
Getting started
Getting started
Installing LLM Compressor
Key Models
Key Models
Llama 4
Llama 4
FP8 Example
Qwen3
Qwen3
FP8 Example
Kimi-K2
Kimi-K2
FP8 Example
Mistral Large 3
Mistral Large 3
FP8 Example
Guides
Guides
Compression Schemes
Sequential Onloading
Model Loading
Distributed Oneshot
Saving a Model
Observers
Memory Requirements
Runtime Performance
Examples
Examples
AutoRound Quantization
AWQ Quantization
Big Model Quantization with Sequential Onloading
Disk offloading
Model-free Quantization
Multimodal Audio Model Quantization
Multimodal Vision-Language Quantization
KV Cache Quantization
Non-uniform Quantization
fp4 Quantization with NVFP4
int4 Weight Quantization
fp8 Weight and Activation Quantization
int8 Weight and Activation Quantization
Quantizing Mixture of Experts (MoE) models
2:4 Sparsity with FP8 Quantization
Applying Transforms to Improve Quantization Accuracy
Experimental
Experimental
Attention Quantization in LLM Compressor
Mistral-format model compression (experimental)
MXFP4 Quantization
Developer
Developer
LLM Compressor Code of Conduct
Contributing to LLM Compressor
API Reference
API Reference
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