# StudioTV LLM VRAM Calculator (remote · studiotvai.com)

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

- Trust score: 83/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- remote · `studiotvai.com`: 83/100 (this document), [markdown](https://verifymcp.io/servers/com-studiotvai-llm-vram/api-mcp.md), [page](https://verifymcp.io/servers/com-studiotvai-llm-vram/api-mcp)

## Channel facts

- Endpoint: `https://studiotvai.com/api/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-05.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 62/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 587 tokens (~195/item across 3 items; 3 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 83/100
  - Stability observed for 25 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 3 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 4 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the StudioTV LLM VRAM Calculator MCP server?

StudioTV LLM VRAM Calculator is a hosted endpoint at https://studiotvai.com/api/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http com-studiotvai-llm-vram 'https://studiotvai.com/api/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "com-studiotvai-llm-vram": {
      "url": "https://studiotvai.com/api/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "com-studiotvai-llm-vram": {
      "type": "http",
      "url": "https://studiotvai.com/api/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.com-studiotvai-llm-vram]
url = "https://studiotvai.com/api/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-studiotvai-llm-vram": {
      "type": "remote",
      "url": "https://studiotvai.com/api/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-studiotvai-llm-vram --url 'https://studiotvai.com/api/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-studiotvai-llm-vram:
    url: "https://studiotvai.com/api/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "com-studiotvai-llm-vram": {
      "Transport": "http",
      "Url": "https://studiotvai.com/api/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add com-studiotvai-llm-vram -t streamable-http -u 'https://studiotvai.com/api/mcp'
```

### Other

```json
{
  "mcpServers": {
    "com-studiotvai-llm-vram": {
      "type": "http",
      "url": "https://studiotvai.com/api/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-05 (score 83, +1)

No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-03 (score 82, +1)

No change was recorded against any check on this day. Stability & Change Management went from 73 to 77. That category is still filling its 30-day observation window: 22 days of observed history at the previous scan, 23 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-31 (score 81, +1)

No change was recorded against any check on this day. Stability & Change Management went from 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-29 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 57 to 60. That category is still filling its 30-day observation window: 17 days of observed history at the previous scan, 18 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-27 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 50 to 53. That category is still filling its 30-day observation window: 15 days of observed history at the previous scan, 16 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-25 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-23 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-21 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (3)

### `estimate_vram` (~301 tokens)

Does this LLM fit on this GPU?

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).

Input parameters:

- `concurrent_requests` (integer): Requests served at the same time, each with its own KV cache. Default 1.
- `context` (integer): Tokens per request (prompt + output). Default 8192.
- `engine` (string): Serving engine. Default: vllm on data-center GPUs, llamacpp elsewhere.
- `gpu` (string): GPU id or name, e.g. "rtx-4090", "H100", "mac-m4-max-128". Default h100. List: https://studiotvai.com/api/gpus.json
- `gpu_count` (integer|string): Number of GPUs, or "auto" (default) for the fewest that fit.
- `kv_cache` (string): KV cache precision. Default bf16.
- `model` (string, required): Model name or id ("Llama 3.3 70B", "qwen3.8-27b"), Hugging Face id ("Qwen/Qwen3-32B") or link.
- `weights` (string): Weight format. Default: the official checkpoint (native) or BF16.

### `models_that_fit` (~169 tokens)

Which LLMs fit on this GPU?

Every listed open model that fits on the given GPU(s), largest first, with the most faithful weight format that fits.

Input parameters:

- `concurrent_requests` (integer): Requests served at the same time, each with its own KV cache. Default 1.
- `context` (integer): Tokens per request (prompt + output). Default 8192.
- `engine` (string): Serving engine. Default: vllm on data-center GPUs, llamacpp elsewhere.
- `gpu` (string, required): GPU id or name, e.g. "rtx-4090".
- `gpu_count` (integer): Default 1.
- `kv_cache` (string): KV cache precision. Default bf16.
- `weights` (string): Weight format. Default: the official checkpoint (native) or BF16.

### `gpu_prices` (~69 tokens)

GPU rental 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.

Input parameters:

- `gpu` (string): GPU id or name. Omit for every GPU.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-studiotvai-llm-vram/api-mcp#diagnostics

## Score history

- 2026-11-05: 83
- 2026-11-04: 82
- 2026-11-03: 82
- 2026-11-02: 81
- 2026-11-01: 81
- 2026-10-31: 81
- 2026-10-30: 80
- 2026-10-29: 80
- 2026-10-28: 79
- 2026-10-27: 79
- 2026-10-26: 78
- 2026-10-25: 78
- 2026-10-24: 77
- 2026-10-23: 77
- 2026-10-22: 76
- 2026-10-21: 76
- 2026-10-20: 75
- 2026-10-19: 75
- 2026-10-18: 74
- 2026-10-17: 74
- 2026-10-16: 74
- 2026-10-15: 73
- 2026-10-14: 73
- 2026-10-13: 72
- 2026-10-12: 72
- 2026-10-11: 71

## Common questions

### What is the StudioTV LLM VRAM Calculator MCP server?

StudioTV LLM VRAM Calculator is an MCP server listed in the public MCP registry as com.studiotvai/llm-vram. Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model. This page covers its hosted endpoint (https://studiotvai.com/api/mcp).

### Is the StudioTV LLM VRAM Calculator MCP server safe to use?

StudioTV LLM VRAM Calculator scores 83 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the StudioTV LLM VRAM Calculator MCP server expose?

StudioTV LLM VRAM Calculator exposes 3 tools: estimate_vram, models_that_fit, gpu_prices. Their descriptions and schemas cost roughly 539 tokens of context every time the server is loaded.

### Does the StudioTV LLM VRAM Calculator MCP server require authentication?

No. We connected to StudioTV LLM VRAM Calculator without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the StudioTV LLM VRAM Calculator MCP server still maintained?

StudioTV LLM VRAM Calculator is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://studiotvai.com/api/mcp
- Website: https://studiotvai.com/llm-vram-calculator
- Changelog RSS feed: https://verifymcp.io/servers/com-studiotvai-llm-vram/api-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-studiotvai-llm-vram/api-mcp.json
- HTML version of this page: https://verifymcp.io/servers/com-studiotvai-llm-vram/api-mcp