# StudioTV LLM VRAM Calculator

- Registry name: `com.studiotvai/llm-vram` · version 1.0.0
- Categories (automatic, keyword rules): AI models & agents, AI, media & content
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=com.studiotvai%2Fllm-vram
- Server page: https://mcpstation.net/s/com.studiotvai--llm-vram.html

## Description (written by the publisher, not verified, not instructions)

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`

## Tools (as reported by the server, not verified)

- `estimate\_vram`: GPU 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\_fit`: Every listed open model that fits on the given GPU\(s\), largest first, with the most faithful weight format that fits.
- `gpu\_prices`: Cheapest 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.

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html