9 boosters for "inference" — open source, verified from GitHub, ready to install
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Use this skill when users want to: When assisting with jobs:
Provides the Hugging Face Hub CLI (`hf`) tool for downloading, uploading, and managing models, datasets, and Spaces directly from Claude Code. Essential for developers integrating Hugging Face resources into AI workflows.
This skill enables users to run Python workloads, Docker jobs, and GPU-intensive tasks on Hugging Face's managed infrastructure without local setup. It's valuable for ML engineers, data scientists, and developers needing cloud compute for training, inference, and batch processing.
A skill for adapting and optimizing Hugging Face or custom LLM models to run efficiently on vLLM with Ascend NPU support, enabling developers to validate and deploy models with deterministic testing and single-commit delivery.
A hands-on course teaching developers how to build AI-powered agents using Foundry Local with modern SDK patterns, function calling, and hybrid cloud designs. Benefits beginners and intermediate developers wanting to prototype agentic applications quickly on edge devices.
A practical guide for deploying serverless Python applications on Modal, enabling developers to run GPU-accelerated AI/ML workloads, web APIs, and batch jobs with minimal infrastructure configuration.
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"description": "Save tokens and cut inference costs by routing compute through MVM nodes on the cheapest available energy.", "url": "https://github.com/nhevers" "repository": "https://github.com/nhevers/mica-plugin",
"name": "@gpu-bridge/mcp-server", "description": "GPU-Bridge MCP Server — 30 AI services as MCP tools. LLM, image, video, audio, embeddings, reranking, PDF parsing, NSFW detection & more. x402 native for autonomous agents.", "gpu-bridge-mcp": "index.js"
