Impressive paper from CMU.
This is a AI post classified by AGK Intelligence as AI infra & evals (research), kept by the AI Radar because it carries real work, not commentary.
Impressive paper from CMU. Soon, we will have coding agents that can tune the entire stack, from prompts to token serving. This work experiments with coding agents to tune an LLM serving stack. They get Claude Sonnet 5.5 to build a serving system for Gemma E4B that delivers 4.87x the value per GPU dollar of the vLLM reference. ServerBench asks coding agents to design a complete, specialized serving system and decide how many concurrent sessions to run. The score is latency-qualified token value per dollar of GPU cost, measured by replaying real coding sessions with their dependencies intac
Posted by elvis (328.1k followers) 1 days ago · 114 likes · 9k views · view the original post on X. Kept by the AI Radar as AI infra & evals. Tools mentioned: arXiv.org, academy.dair.ai.
More AI work like this
- Enterprise AI is only as useful as the data and business context behind it. — @databricks
- DecodeWall: another benchmark I'm building, focused on concurrent calls. — @ivanfioravanti
- KindlingAI's GLM 5.3 Flash for one Spark recipe is here! 700k total KV on DGX OS (up to… — @mmastrac
- A small model coaching a giant? Aiona checks the held-out tie and SMF’s memory-card… — @MichaelGannotti
- Ranking inference providers six ways — @AdamHoltererer
- Harness adapters training is now live! — @yb2698
- NVIDIA is giving Kimi K3.1 Pro away for free. — @VaibhavSisinty
- The world’s first co-packaged optics Ethernet switch is in full production. — @NVIDIAAIInfra
Every post is read and classified by AGK Intelligence: what it is, which market it belongs to, and whether the link is a real tool. 65.6k posts from 5k X accounts over the last 14 days, 10.4k tools, 19 markets. New posts are collected several times an hour and the full ranking is recomputed every hour — last update 2026-11-06 16:05 UTC. Full method.