Best MCP servers for model hosting
Running machine-learning models, hosted inference endpoints, model registries, local runtimes, is a moving target, and an AI agent that can reach your model-hosting platforms can deploy, query, and compare models without a human stitching together APIs. An agent might run inference against a hosted endpoint, look up a model on a registry, or check what is available on a local runtime, all in one workflow. The right server depends on how you serve models: an open model hub, an inference marketplace, a fast hosted-inference provider, or a local runtime for private models. The servers below are real MCP servers for popular model-hosting platforms, each with a verified install config, so an agent can work with models directly.
Hugging Face
Hugging Face
Hugging Face's official MCP server: search and explore models, datasets, Spaces, papers, and docs from your AI assistant.
Hugging Face's server gives an agent access to the largest open model and dataset hub, ideal for discovering models, reading model cards, and reasoning about what to deploy.
Replicate
Replicate
Replicate's official MCP server: discover, compare, and run thousands of hosted AI models — image, video, audio, and language — straight from your agent.
Replicate's server lets an agent run hosted models via a simple API, strong for invoking open-source models for inference without managing GPUs yourself.
Baseten
Baseten
Baseten's official MCP servers give an agent live access to your model deployments and Baseten's docs: deploy, call, and operate models from your editor.
Baseten's server suits teams deploying and serving their own models on dedicated infrastructure, letting an agent reason about and invoke production inference endpoints.
Ollama
hyzhak (community)
Maintained Ollama MCP server: pull, run, and chat with local LLMs, manage models, and call an OpenAI-compatible chat API on a private Ollama instance.
Ollama's server connects an agent to locally run models, the right choice when you need private, offline inference on your own hardware.