Deploy Open-Source Models with Hugging Face Inference Endpoints

Melvin Vivas · X post · 2026-10-02 · Open on X

Topics: LLMOps, Deployment & Monitoring · Level: intermediate

Summary

The creator recommends Hugging Face Inference Endpoints as the easiest current way to deploy open-source models. It lets you run AI workloads on AWS, Azure or Google Cloud. A related post from him puts a 27B model at about $2.50/hr.

Key points

• Hugging Face Inference Endpoints is a managed service for deploying open-source models.
• You can pick AWS, Azure or Google Cloud to run the workload.
• The creator calls it the easiest way to deploy open-source models right now.
• Cost note from a related post: about $2.50/hr for a ~27B model (Qwen3.8-27B), so plan your budget.

Resources mentioned

• Hugging Face Inference Endpoints · tool · endpoints.huggingface.co · paid
  Managed service for deploying models from the Hugging Face Hub on dedicated infrastructure.
  Also in: Deploying Qwen3.8 27B on Hugging Face Inference Endpoints (Melvin Vivas on X · notes), Using Hugging Face credits: Jobs, Inference Endpoints and Open Models (Melvin Vivas on X · notes)
• Hugging Face · website · x.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Platform for hosting and finding ML models, datasets and papers. The quoted post says LocateAnything was trending there.
  Also in: Using an ML agent to train an open-source TTS model on your voice (Melvin Vivas on X · notes), Deploying Qwen3.8 27B on Hugging Face Inference Endpoints (Melvin Vivas on X · notes), Using Hugging Face credits: Jobs, Inference Endpoints and Open Models (Melvin Vivas on X · notes), Unsloth passes 500M model downloads on Hugging Face (Melvin Vivas on X · notes) and 38 more

Try this

• Try deploying an open-source model with Hugging Face Inference Endpoints.