Home / Companies / Together AI / Blog / Post Details
Content Deep Dive

Even Better, Even Faster Quantized LLMs with QTIP

Blog post from Together AI

Post Details
Company
Date Published
Author
Albert Tseng, Qingyao Sun, David Hou, Chris De Sa
Word Count
3,170
Company Posts That Month
6
Language
English
Hacker News Points
-
Post removed?
No
Summary

QTIP (Quantization with Trellises and Incoherence Processing) is a new weight-only LLM post-training quantization method that achieves state-of-the-art quality and inference speed. It compresses model weights using trellis coded quantization, which significantly improves over QuIP#'s quality while being 3X faster than unquantized models. QTIP builds upon the incoherence processing framework introduced by QuIP and uses trellis coded quantization to achieve lower distortion on i.i.d Gaussian sources compared to vector quantization. The bitshift trellis and compute-based codes enable fast decoding for weight-only quantization, making QTIP practical for memory-bound inference settings.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
LLM 9 3,988 514 165 -1%
AI Model Fine-tuning 1 918 172 83 +34%
Use This Data

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.