Revyzor — KV Cache Compression That Actually Works
1.45x lossless bit-exact in under 50ms. 3–5x standard with zero accuracy loss. Drops into vLLM, SGLang & NIM with one config flag. Validated on H100, A100, L40S.
TurboQuant scores 0.9% on GSM8K • FP8 scores 4/35 at 128K • The only production-ready KV cache compression
Learn More at Revyzor →- Bookmark stories with reactions via GitHub
- Comment on any post — no account needed to read
- Write your own posts or guides
Recent Posts
-
From Specialists to Builders: How AI Agentic Coding Is Reshaping Software Teams
An analysis of how agentic AI systems are transforming software development workflows, with implications for teams deploying local LLMs in development environments.
-
Good LLM Development and Usage Patterns
A practical guide outlining recommended patterns for developing and deploying LLMs in production environments, covering best practices for local and self-hosted inference.
-
JetBrains Releases Mellum2: A 12B MoE Model for Fast, Specialized Tasks
JetBrains introduces Mellum2, a 12-billion parameter mixture-of-experts model designed for efficient local inference in multi-model AI pipelines. The model balances performance and resource consumption for on-device deployment scenarios.