Eldar Kurtić's contributions
Article
Optimized support for Open NVIDIA Nemotron 3.5 Super VL
Double Nemotron 3.5 Super VL inference throughput on NVIDIA Hopper GPUs. Learn how FP8 quantization and DSpark speculative decoding boost speed and accuracy.
Article
Run Mistral Large 3 & Ministral 3 on vLLM with Red Hat AI on Day 0: A step-by-step guide
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.
Article
Speculators: Standardized, production-ready speculative decoding
Speculators standardizes speculative decoding for large language models, with a unified Hugging Face format, vLLM integration, and more.
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
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.
Optimized support for Open NVIDIA Nemotron 3.5 Super VL
Double Nemotron 3.5 Super VL inference throughput on NVIDIA Hopper GPUs. Learn how FP8 quantization and DSpark speculative decoding boost speed and accuracy.
Run Mistral Large 3 & Ministral 3 on vLLM with Red Hat AI on Day 0: A step-by-step guide
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: Standardized, production-ready speculative decoding
Speculators standardizes speculative decoding for large language models, with a unified Hugging Face format, vLLM integration, and more.
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.
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.
