Deploy Hugging Face LLMs on Google Cloud Run with Serverless GPUs

This article details a method for deploying Hugging Face language models on Google Cloud Run using serverless GPUs. It outlines a streamlined process involving a Makefile, Dockerfile, and Terraform scripts to automate the build, provisioning, and deployment of models like Qwen/Qwen3.5-4B. The approach focuses on baking model weights into the Docker image at build time, ensuring no runtime downloads and enabling efficient, self-contained deployments on NVIDIA L4 GPUs with an OpenAI-compatible API. AI

IMPACT Enables efficient, cost-effective deployment of LLMs for developers without deep infrastructure expertise.

RANK_REASON The article describes a method for deploying existing models on a cloud platform, which is a tooling-related use case.

Read on dev.to — LLM tag →

• Artifact Registry
• Dockerfile
• Google Cloud
• Google Cloud Run
• Hugging Face
• Makefile
• NVIDIA L4 GPU
• Qwen/Qwen3.5-4B
• Terraform
• vLLM

• infra
• product

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