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runpod-deployment

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Deploy GPU workloads to RunPod serverless.

Software Engineering#serverless#cost-optimization#autoscaling#openai-compatible#vllm#runpod#gpu-deployment
AuthorScientiaCapital
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Deploy GPU workloads on RunPod using serverless, vLLM endpoints, and pod-based compute to simplify and accelerate deployment workflows.

Core Features & Use Cases

  • Serverless Workers: Scale-to-zero handlers with pay-per-second billing for cost-effective inference.
  • vLLM Endpoints: OpenAI-compatible LLM serving with high throughput.
  • Pod Management: Dedicated GPU instances for development and training with flexible lifecycle.
  • Cost Optimization & Monitoring: GPU selection, spot instances, and health monitoring to optimize budgets across regions.

Quick Start

To deploy a RunPod serverless endpoint with GPU support, run the deployment workflow using the provided GitHub Actions or CLI templates.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: runpod-deployment
Download link: https://github.com/ScientiaCapital/skills/archive/main.zip#runpod-deployment

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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