MCP server

AI Compute Radar Official

dev.aicomputeradar/ai-compute-radar

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Working at last checkVerified publisher: aicomputeradar.devAbout 951 tokens per request once connected5 tools

https://aicomputeradar.dev/api/mcp

At a glance

Type: MCP server
Publisher: aicomputeradar.dev (verified by the MCP Registry)
Status: Working at last check
Tools: 5
Token cost: About 951 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.ai-compute-radar]
url = "https://aicomputeradar.dev/api/mcp"

Gemini CLI

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

Cursor

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

VS Code

{"servers":{"ai-compute-radar":{"type":"http","url":"https://aicomputeradar.dev/api/mcp"}}}

Antigravity

agy mcp add ai-compute-radar https://aicomputeradar.dev/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ai-compute-radar","server_url":"https://aicomputeradar.dev/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ai-compute-radar","server_url":"https://aicomputeradar.dev/api/mcp"}

find_fit ~253 tokens
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 ~252 tokens
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 ~79 tokens
Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.
trending_models ~172 tokens
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.
weekly_pick ~195 tokens
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

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