AI Compute Radar

dev.aicomputeradar/ai-compute-radar · v1.2.0 · agents-and-memory

AI Compute Radar is healthy: it completed an MCP handshake in 764 ms and exposes 5 tools.

Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.

Endpoint: https://aicomputeradar.dev/api/mcp (streamable-http)

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Facts

Checked (UTC)	Status	Latency	Error
2026-10-16T01:50:12.357Z	Healthy	764
2026-10-14T23:10:42.173Z	Healthy	2578

`trending_models`	Tracked AI models ranked by Heat Score (0–100, weighted percentiles of measured Hugging Face/OpenRouter signals) with the raw signals, local-run facts (GGUF size, quantization) and links. Models still collecting a week of history have heat=null and rank after scored ones.
`find_fit`	Which tracked models run on a given GPU or Mac: measured GGUF weights + computed context cache + runtime overhead versus usable memory. Returns the best recommendation and every verdict (EXCELLENT/GOOD/TIGHT/OFFLOAD_REQUIRED/NOT_RECOMMENDED/UNKNOWN) with plain-language reasons. Get hardware ids from
`gpu_prices`	Median verified on-demand rental price per GPU class on Vast.ai (USD per hour), with min/p75 and offer counts, the collection timestamp, and per class the Rent Index: this week's median against last week and against the first week collected, a trend word, and the days excluded as marketplace glitche
`list_hardware`	Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.
`weekly_pick`	The current pick of the week: one tracked model chosen by a published rule (largest counted Heat Score rise among models that run comfortably on a consumer card of up to 24 GB), with the numbers frozen at selection time, a device-by-device fit ladder and the written report including its caveats. Pas