vMLX: Fast MLX Models
Use Python to quickly make super fast machine learning models.
ML models that can run on a startup's MCP server and handle big datasets.
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.
"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."
Start with vMLX if you're new to machine learning and want a simple, free way to get started.
Reach for vMLX when you need a custom ML solution on an existing MCP server, and want to save time and resources.
The use of 'vMLX' might be confused with MLX in general. This skill specifically addresses fast MLX models for MCP servers.
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
git clone https://github.com/jjang-ai/vmlx && cd vmlx- โขWhen you need to deploy large machine learning models and want to reduce memory usage
- โขWhen you require fast model serving and inference with low latency
- โขWhen you want to survive model serving interruptions, such as restarts, with a disk cache
- 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
curl -X POST -H 'Content-Type: application/json' -d '@input.json' http://localhost:8000/predict
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