Modal Serverless GPU

Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

• terminal => bash
• web_extract => web_fetch, plus web_search when discovery is needed
• search_files => grep_search and glob_search
• browser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.

Modal Serverless GPU

Comprehensive 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