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StudioTV LLM VRAM Calculator
com.studiotvai/llm-vram · v1.0.0
Measured by MCP Station
| Last check | Reachable |
|---|---|
| Checked at | — |
| Check success 7 / 30 days | — |
| First-try success 30 days | — |
| Median response | — |
| Lookups 30 days | — |
| Authentication | No auth |
| MCP version | 2025-06-18 |
| Rank score | — |
| Declared risk | Read-only (all tools declare readOnlyHint) |
| Capabilities declared | tools |
| Tool definitions | 3 tools · with description 100% · input schema is an object 100% · read/destructive hints 100% |
Description written by the publisher, not verified
Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model.
Connect
streamable-http · https://studiotvai.com/api/mcp
claude mcp add --transport http llm-vram https://studiotvai.com/api/mcp{
"mcpServers": {
"llm-vram": {
"url": "https://studiotvai.com/api/mcp"
}
}
}Tools as reported by the server
estimate_vramGPU memory, number of GPUs, speed and rental cost to run an open LLM. Works for the models listed at https://studiotvai.com/api/models.json and any Hugging Face model id or link. Uses the real KV cache of each architecture (sliding window, hybrid linear attention, MLA).models_that_fitEvery listed open model that fits on the given GPU(s), largest first, with the most faithful weight format that fits.gpu_pricesCheapest on-demand price per GPU-hour from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour.
Website · JSON · Text for agents · Registry updated 2026-10-10