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AI Compute Radar

dev.aicomputeradar/ai-compute-radar · v1.2.1

Measured by MCP Station

Description written by the publisher, not verified

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

Connect

claude mcp add --transport http ai-compute-radar https://aicomputeradar.dev/api/mcp

{
  "mcpServers": {
    "ai-compute-radar": {
      "url": "https://aicomputeradar.dev/api/mcp"
    }
  }
}

Tools as reported by the server

• 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

Website · JSON · Text for agents · Registry updated 2026-10-22