mcp-server

vMLX: Fast MLX Models

Name: vMLX: Fast MLX Models
Author: jjang-ai

Get super fast MLX models with vMLX, ideal for startup founders using MCP servers, built with Python.

intermediate⏱ 30 minutes💵 Free

771 stars83 forksPythonQuality 8/10Updated 7/16/2026100% free · open source

What it is

Use Python to quickly make super fast machine learning models.

What you can make with it

ML models that can run on a startup's MCP server and handle big datasets.

How it helps

vMLX lets you easily get started with fast ML models without needing a lot of expertise, freeing up time to focus on your core product. It's especially useful for founders with limited resources.

Real use case example

"A founder wants to add product recommendations to their e-commerce site. They use vMLX to quickly build a fast ML model, which runs on their MCP server. They train the model with a dataset of customer purchases and product features. The model helps their site suggest relevant products to each customer."

If you're new

Start with vMLX if you're new to machine learning and want a simple, free way to get started.

If you're senior

Reach for vMLX when you need a custom ML solution on an existing MCP server, and want to save time and resources.

Common confusion cleared up

The use of 'vMLX' might be confused with MLX in general. This skill specifically addresses fast MLX models for MCP servers.

Best inside these AI tools

CursorClaude Desktop

Pairs with

Stripe webhooks

Why we list it on WorkflowStacks: vMLX is free and open-source, making it a cost-effective option for startups or individuals who need fast ML models.

What it does

vMLX is a Python tool that provides an optimized and compressed model serving solution with L2 disk cache, L1 paged cache, and hybrid scheduler for fast and efficient model deployment

Install / run

git clone https://github.com/jjang-ai/vmlx && cd vmlx

When to use it

•When you need to deploy large machine learning models and want to reduce memory usage
•When you require fast model serving and inference with low latency
•When you want to survive model serving interruptions, such as restarts, with a disk cache

Quick start

1Clone the vMLX repository and navigate to the project directory
2Install the required dependencies with `pip install -r requirements.txt`
3Configure the model serving settings in the `config.json` file
4Start the vMLX server with `python app.py`
5Test the model serving with a sample request using `curl` or a tool like Postman

Ready-to-paste prompt

curl -X POST -H 'Content-Type: application/json' -d '@input.json' http://localhost:8000/predict

Heads up: Make sure you have the correct Python version installed, as vMLX has specific dependencies and may not be compatible with all Python versions

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