# ppb-mcp

> Use this tool when you need to optimize GPU inference performance for large language models, as it provides queryable benchmark data on quantization, throughput, VRAM, and concurrent users. This enables informed decisions on resource allocation and model deployment, solving problems related to efficiency and scalability. It accepts LLM client queries as input and returns relevant benchmark data as output, ideal for use cases requiring data-driven optimization of GPU-based inference workloads.

Canonical page: https://skillsregistry.net/skills/paulplee-ppb-mcp  
JSON: https://api.skillsregistry.net/v1/skills/paulplee-ppb-mcp

## Description

Exposes queryable GPU inference benchmark data (quantization, throughput, VRAM, concurrent users) as tools for LLM clients.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/r8a0s6t52i)
- **Repository:** <https://github.com/paulplee/ppb-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "paulplee-ppb-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/paulplee-ppb-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/paulplee-ppb-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt