infrastructure-modal

Topics in the source

• Modal Serverless GPU
• When to use Modal
• Quick start
• Installation
• Hello World with GPU
• Basic inference endpoint
• Core concepts
• Key components

Comprehensive guide to running ML workloads on Modal's serverless GPU cloud platform. Use Modal when: Running GPU-intensive ML workloads without managing infrastructure Deploying ML models as auto-scaling APIs Running batch processing jobs (training, inference, data processing) Need pay-per-second GPU pricing without idle costs Prototyping ML applications quickly Running scheduled jobs (cron-like workloads) Key features: Serverless GPUs: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand Python-native: Define infrastructure in Python code, no YAML Auto-scaling: Scale to zero, scale to 100+ GPUs instantly Sub-second cold starts: Rust-based infrastructure for fast container launches Container caching: Image layers cached for rapid iteration Web endpoints: Deploy functions as REST APIs with zero-downtime updates Use alternatives instead: RunPod: For longer-running pods with persistent state Lambda Labs: For reserved GPU instances SkyPilot: For multi-cloud orchestration and cost optimization Kubernetes: For complex multi-service architectures Run: modal run hellogpu.py | Component | Purpose | |-----------|---------| | App | Container for functions and resources | | Function | Serverl

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