8 posts
We are excited to announce that AutoRound — Intel's state-of-the-art post-training quantization (PTQ) algorithm — is now fully integrated into vLLM-Omni, enabling a streamlined quantize-once,...
As organizations increasingly adopt AI-powered development tools, the need for high-performance agentic models that deliver both accuracy and operational efficiency has become critical. Laguna...
TurboQuant, a method for KV-cache quantization, recently gained significant traction in the community due to the large advertised savings in GPU memory from very low bit-width quantization of a...
Long-context LLM serving is increasingly memory-bound: for standard full-attention decoders, the KV cache often dominates GPU memory at 128k+ contexts, and each decode step must read a large...
DeepSeek-V3.2 (NVFP4 + TP2)has been successfully and smoothly run on GB300 (SM103 - Blackwell Ultra). Leveraging FP4 quantization, it achieves a single-GPU throughput of 7360 TGS (tokens / GPU /...
Achieve faster, more efficient LLM serving without sacrificing accuracy!
TL;DR: vLLM on AMD ROCm now has better FP8 performance!
Today, the vLLM team is excited to partner with Meta to announce the support for the Llama 3.1 model series. Llama 3.1 comes with exciting new features with longer context length (up to 128K...
