Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model.
Connect Remote server
claude mcp add --transport http llm-vram https://studiotvai.com/api/mcp
{
"mcpServers": {
"llm-vram": {
"url": "https://studiotvai.com/api/mcp"
}
}
}
{
"servers": {
"llm-vram": {
"type": "http",
"url": "https://studiotvai.com/api/mcp"
}
}
}
Settings > Connectors > Add custom connector
URL: https://studiotvai.com/api/mcp
Tools 3 found in our last check
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.
Details
| Endpoint | https://studiotvai.com/api/mcp (Streamable HTTP) |
| First published | 10 Oct 2026 |
| Last updated | 10 Oct 2026 |
| Registry name | com.studiotvai/llm-vram |
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