Dipika Sikka

Learn about NVFP4, a 4-bit floating-point format for high-performance inference on modern GPUs that can deliver near-baseline accuracy at large scale.

Explore the latest release of LLM Compressor, featuring attention quantization, MXFP4 support, AutoRound quantization modifier, and more.

Run the latest Mistral Large 3 and Ministral 3 models on vLLM with Red Hat AI, providing day 0 access for immediate experimentation and deployment.

Speculators standardizes speculative decoding for large language models, with a unified Hugging Face format, vLLM integration, and more.

The LLM Compressor 0.8.0 release introduces quantization workflow enhancements, extended support for Qwen3 models, and improved accuracy recovery.

LLM Compressor 0.7.0 brings Hadamard transforms for better accuracy, mixed-precision FP4/FP8, and calibration-free block quantization for efficient compression.

Discover how to deploy compressed, fine-tuned models for efficient inference with the new Axolotl and LLM Compressor integration.

Optimize model inference and reduce costs with model compression techniques like quantization and pruning with LLM Compressor on Red Hat OpenShift AI.