Mark Kurtz's contributions
Article
Enable 3.5 times faster vision language models with quantization
Learn how quantized vision-language models enable faster inference, lower costs, and scalable AI deployment without compromising capability.
Article
Deployment-ready reasoning with quantized DeepSeek-R1 models
Explore new open source quantized reasoning models based on the DeepSeek-R1-Distill suite that deliver near-perfect accuracy and inference speed improvements.
Article
2:4 Sparse Llama: Smaller models for efficient GPU inference
Discover Sparse Llama: A 50% pruned, GPU-optimized Llama 3.1 model with 2:4 sparsity, enabling faster, cost-effective inference without sacrificing accuracy.
Article
Multimodal model quantization support through LLM Compressor
Explore multimodal model quantization in LLM Compressor, a unified library for optimizing models for deployment with vLLM.
Article
Compressed Granite 3.1: Powerful performance in a small package
Open-sourced on Hugging Face, deployment-ready with vLLM, and extensible using LLM Compressor.
Article
2:4 Sparse Llama FP8: SOTA performance for NVIDIA Hopper GPUs
Advancing AI efficiency is more critical than ever, and sparsity has proven to be a cornerstone in this pursuit.
Article
We ran over half a million evaluations on quantized LLMs—here's what we found
Quantized LLMs achieve near-full accuracy with minimal trade-offs after 500K+ evaluations, providing efficient, high-performance solutions for AI model deployment.
Article
LLM Compressor is here: Faster inference with vLLM
Discover LLM Compressor, a unified library for creating accurate compressed models for cheaper and faster inference with vLLM.
Enable 3.5 times faster vision language models with quantization
Learn how quantized vision-language models enable faster inference, lower costs, and scalable AI deployment without compromising capability.
Deployment-ready reasoning with quantized DeepSeek-R1 models
Explore new open source quantized reasoning models based on the DeepSeek-R1-Distill suite that deliver near-perfect accuracy and inference speed improvements.
2:4 Sparse Llama: Smaller models for efficient GPU inference
Discover Sparse Llama: A 50% pruned, GPU-optimized Llama 3.1 model with 2:4 sparsity, enabling faster, cost-effective inference without sacrificing accuracy.
Multimodal model quantization support through LLM Compressor
Explore multimodal model quantization in LLM Compressor, a unified library for optimizing models for deployment with vLLM.
Compressed Granite 3.1: Powerful performance in a small package
Open-sourced on Hugging Face, deployment-ready with vLLM, and extensible using LLM Compressor.
2:4 Sparse Llama FP8: SOTA performance for NVIDIA Hopper GPUs
Advancing AI efficiency is more critical than ever, and sparsity has proven to be a cornerstone in this pursuit.
We ran over half a million evaluations on quantized LLMs—here's what we found
Quantized LLMs achieve near-full accuracy with minimal trade-offs after 500K+ evaluations, providing efficient, high-performance solutions for AI model deployment.
LLM Compressor is here: Faster inference with vLLM
Discover LLM Compressor, a unified library for creating accurate compressed models for cheaper and faster inference with vLLM.
