A compressed summary
- As computational costs for large language models continue to rise, research has demonstrated that LLMs can be compressed and optimized to run with far fewer compute and energy resources, without degradation in model quality.
- LLM Compressor enables several state-of-the-art compression techniques, ranging from simple quantization and pruning to calibrated compression.
- Red Hat OpenShift AI allows ML Engineers and Data Scientists to compare and evaluate several model compression techniques in a single, shareable experiment, customized to their use case, model and dataset.
LLMs continue to make breakthroughs in language modeling tasks. However, as models continue to increase in size and complexity, the computational and memory costs involved in deploying them have become a barrier to their accessibility, even for organizations with access to high-end GPUs. Recent examples include Meta’s Llama 4 Scout and Maverick models, which surpass 100 billion and 400 billion parameters, respectively.
In order to help further optimize model inference and reduce the cost of model deployment, research efforts have focused on model compression to reduce model size without sacrificing performance. As AI applications mature and new compression algorithms are published, there is a need for unified tooling which can accomplish a variety of compression methods, specific to a user’s inference needs and optimized to run performantly on their accelerated hardware.
Introduction to LLM Compressor
LLM Compressor, part of the vLLM project for efficient serving of LLMs, integrates the latest model compression research into a single open-source library enabling the generation of efficient, compressed models with minimal effort.
The framework allows users to apply some of the most recent research on model compression techniques to improve generative AI (gen AI) models' efficiency, scalability and performance while maintaining accuracy. With native support for Hugging Face and vLLM, the compressed models can be integrated into deployment pipelines, delivering faster and more cost-effective inference at scale.
LLM Compressor supports a wide variety of compression techniques:
- Weight-only quantization (W4A16) compresses model weights to 4-bit precision, valuable for AI applications with limited hardware resources or high sensitivity to latency.
- Weight and activation quantization (W8A8) compresses both weights and activations to 8-bit precision, targeting general server scenarios for integer and floating point formats. Most recently, Meta’s Llama 4 Maverick FP8 model was quantized using LLM Compressor.
- Weight pruning, also known as sparsification, removes certain weights from the model entirely. While this requires fine-tuning, it can be used in conjunction with quantization for further inference acceleration.
While each method has varying data and algorithmic requirements, all can be applied directly using the Red Hat OpenShift AI platform, either through an interactive workbench or within the data science pipeline feature.
Integration with Red Hat OpenShift AI
OpenShift AI empowers ML engineers and data scientists to experiment with model training, fine-tuning and now compression. The OpenShift AI integration of LLM compressor, available as a developer preview feature beginning with v2.20, provides two introductory examples:
- A workbench image and notebook that demonstrates the compression of a tiny model, runnable on CPU, highlighting how calibrated compression can improve over data-free approaches.
A data science pipeline that extends the same flow to a larger Llama 3.2 model, highlighting how users can build automated, GPU-accelerated experiments that can be shared with other stakeholders in a single web UI.
The following video recording demonstrates the data science pipeline in the OpenShift AI dashboard:
As AI adoption increases, so too does the need to efficiently deploy LLMs. We hope to have given you a feel for how you can run these experiments yourself with LLM Compressor and vLLM within OpenShift AI. We invite you to experiment with the developer preview here.
Want to learn more about LLM Compressor and vLLM? Check out our GitHub repo for documentation on our compression algorithms, or join the vLLM Slack and connect with us directly in the #llm-compressor Slack channel. We’d love to hear from you.
