RSI-Inference Bench
Intro
**RSI-Inference Bench** is a newly tracked term on FastToolHub's radar. Source signal: X+Web. Radar summary: Apple ML Research introduced RSI-Inference Bench (~Oct 16): benchmark + environments from real vLLM/SGLang inference-engineering tasks (day-0 model support, optimization, bugfix). Best agent 26.7% pass@1 after ~$100K API tokens and 50K GPU hours. Site rsi-inference.com; GH apple-aiml-research/ml-rsi-inference-bench.
Explain
RSI-Inference Bench appears in the model / AI-system category: something teams would load, call, or fine-tune rather than a pure UI gadget. It is framed as an evaluation or leaderboard surface — useful when you need comparable scores instead of anecdotal demos. Mentions point at agent workflows — coding agents, computer-use, or tool-using loops. Infrastructure / hardware angles (clusters, accelerators, factories) are part of the story. Product-launch or platform framing suggests an end-user or developer-facing release, not only a research paper. Do not invent parameters, prices, or benchmarks beyond what the excerpt already states.
Usage
Start from the primary link: https://github.com/apple-aiml-research/ml-rsi-inference-bench. If a GitHub repo is linked, skim README, license, and recent commits before integrating. For benchmarks: reproduce the stated protocol, note the version, and avoid cherry-picking single numbers from social posts. For models: verify context length, license, and hardware needs from official docs; run a small smoke test before production. For agent/product tools: try a narrow workflow first, keep secrets out of prompts, and log failures for later replay. Numbers and claims in this page only repeat the radar excerpt — double-check upstream before publishing.