AI Compute Radar
Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.
사용해야 할까요
품질 및 안전성
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"ai-compute-radar": {
"url": "https://aicomputeradar.dev/api/mcp"
}
}
}원격 엔드포인트
https://aicomputeradar.dev/api/mcpstreamable-http할 수 있는 일
도구 목록
도구 (5)
⚪trending_models(limit, slug)
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.
입력 스키마
{
"type": "object",
"properties": {
"limit": {
"description": "How many models to return (default 12).",
"type": "integer",
"minimum": 1,
"maximum": 100
},
"slug": {
"description": "Return a single model by slug.",
"type": "string"
}
},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢find_fit(hardware, model, context, kv)
Which tracked models run on a given GPU or Mac: measured GGUF weights + computed context cache + runtime overhead versus usable memory. Returns the best recommendation and every verdict (EXCELLENT/GOOD/TIGHT/OFFLOAD_REQUIRED/NOT_RECOMMENDED/UNKNOWN) with plain-language reasons. Get hardware ids from list_hardware.
입력 스키마
{
"type": "object",
"properties": {
"hardware": {
"type": "string",
"description": "Hardware id or page slug, e.g. rtx-4090, mac-studio-m3-ultra-96gb."
},
"model": {
"description": "Restrict to one model slug.",
"type": "string"
},
"context": {
"description": "Context length in tokens (default 8192).",
"type": "integer",
"minimum": 512,
"maximum": 1048576
},
"kv": {
"description": "KV-cache quantization (default f16).",
"type": "string",
"enum": [
"f16",
"q8_0",
"q4_0"
]
}
},
"required": [
"hardware"
],
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢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 glitches, plus RunPod's lowest posted on-demand price per class (a list price, not a median), Clore.ai's median for the same class (a second marketplace, never blended), and the Azure and Oracle Cloud pay-as-you-go list prices per GPU-hour, each with the VM size or bare-metal shape the price sits in (list prices, read four times a day, no statement about capacity). The index describes what prices did; it never forecasts.
입력 스키마
{
"type": "object",
"properties": {},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}🟢list_hardware
Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.
입력 스키마
{
"type": "object",
"properties": {},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}⚪weekly_pick(week)
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. Pass week (e.g. 2026-w37) for a past issue. issue is null until the first issue is published.
입력 스키마
{
"type": "object",
"properties": {
"week": {
"description": "ISO week label of a past issue, e.g. 2026-w37 (default: the current issue).",
"type": "string",
"pattern": "^\\d{4}-w\\d{2}$"
}
},
"$schema": "https://json-schema.org/draft/2020-12/schema"
}커뮤니티
증거