Hugging Face is an AI community building the future. They provide tools that enable users to build, train and deploy ML models based on open source code and technologies.
Reads any public Hugging Face model repository's config.json and metadata to estimate its memory requirements: the attention layout, the cost of each 1,000 tokens of context, memory at every quantisation, and whether the model fits a given GPU. Also backs the built-in model table, which was read from each model's config.json and checked against Hugging Face.
Checks whether an open-weight LLM fits your GPU: VRAM for weights, KV cache and overhead at each quantisation, a tokens-per-second ceiling, the longest context that fits, and what would work instead when it doesn't. Reads any Hugging Face repo's config.json. Free, read-only, no API key.
4
6
MIT
Why this server?
Integrates with Hugging Face for model hosting and distribution, with links to MiniMax AI models on the platform.
JavaScript implementation of MiniMax MCP that enables interaction with MiniMax AI services for image generation, video generation, text-to-speech, and voice cloning through MCP-compatible clients.
10
299 npm
130
MIT
Why this server?
Connects to MiniMax's Hugging Face organization to access related models and resources
Enables MCP clients like Claude Desktop and Cursor to interact with MiniMax APIs for generating speech, cloning voices, creating videos, and generating images.
6
1,583
MIT
Why this server?
Allows importing models from Hugging Face, connecting a personal token for private/gated repositories, and managing the Hugging Face integration for the workspace.
The logs of everything on one machine — app, build, GPU, containers, serial board, OS — interleaved in the order they happened, and readable by an agent. Read-only: it can tail, search and wait, and it cannot write, delete or configure anything.
2
9
6 npm
1
MIT
Why this server?
Provides Hugging Face agents with tools to search and verify MCP servers.
Exposes RNV color workflow tools for color mixing, conversion, harmony generation, text transformation, and palette management, allowing LLMs to directly compute colors and manipulate palettes.
9
1
MIT
Why this server?
Provides local embedding using Hugging Face models for semantic search, with nothing leaving the machine.
Enables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.
15
145 npm
Apache 2.0
Why this server?
Downloads Chatterbox model weights from Hugging Face and caches them locally for text-to-speech synthesis, enabling the server to obtain required models on first use without bundling them.
Enables AI agents to generate spoken audio locally on a GPU via the Chatterbox models, with zero-shot voice cloning from reference clips, multilingual output, and sentence-aligned chunking for long text.
