Connect-MCP

StudioTV LLM VRAM Calculator

com.studiotvai/llm-vram · v1.0.0

Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model.

MCPremoteno credentialsCloud & DevOpsAI & LLMTasks & projects

Reported outcomes

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Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 3 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 100/100

What it does: 3 tools · 3 read, 0 write · AI & LLM, Tasks & projects, Cloud & DevOps

Test call gpu_prices: worked (1,026 ms)

How the rating is made
  • Answers without login: +30
  • Speed of first answer: +10
  • Lists tools: +10
  • Tool descriptions: +15
  • Parameter descriptions: +10
  • Read/write annotations: +5
  • Safe test call: +20
  • More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL
https://studiotvai.com/api/mcp

Tools

3 tools listed at last check
  • 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.

Badge

Connect-MCP badge for com.studiotvai/llm-vram

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