modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
Before you install
- Last update
- Files in folder
- 3
- Scripts
- No script files found
- SKILL.md size
- 8.4 KB (about 2,139 tokens of context each time it loads)
- License
- MIT
- Source on GitHub
- davila7/claude-code-templates
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
Install
npx skills add davila7/claude-code-templates@modal-serverless-gpuRuns in a terminal with Node.js 18 or newer. Add -y to skip the prompts and -g to install for every project instead of this one.
Install for your agent
npx skills add davila7/claude-code-templates@modal-serverless-gpu --agent claude-code -y- Project install writes to
.claude/skills/modal-serverless-gpu- Global install writes to
~/.claude/skills/modal-serverless-gpu
Start a new session afterwards so the agent picks the skill up. In Claude Code you can also call it directly with /modal-serverless-gpu.
What it does
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…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
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