Agent skill · software engineering · nousresearch
modal
Serverless GPU cloud for ML jobs and model APIs.
Why this skill is useful
Provides specific commands and patterns for deploying ML workloads on Modal's serverless GPU platform that the AI wouldn't reliably generate on its own.
What it needs
Requires modal installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 226,679 stars on the source repository.
What this skill does
Modal Serverless GPU Guide to running ML workloads on Modal's serverless GPU cloud platform. When to use Modal 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 Quick start Installation Hello World with GPU Run: modal run hellogpu.py Basic inference endpoint Core concepts Key components Component Purpose ----------- --------- App Container for functions and resources Function Serverless function with compute specs Cls Class-based functions with lifecycle hooks Image Container image definition Volume Persistent storage for models/data Secret Secure credential storage Execution modes Command Description --------- ------------- modal run script.py Execute and exit modal serve script.py Development with live reload modal deploy script.py Persistent cloud deployment GPU configuration Available GPUs GPU VRAM Best For ----- ------ ---------- T4 16GB Budget inference, small models L4 24GB Inference, Ada Lovelace arch A10G 24GB Training/inference, 3.3x faster than T4 L40S 48GB Recommended for inference (best cost/perf) A100-40GB 40GB Large model training A100-80GB 80GB Ve …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/modal