ffmpeg-modal-containers

## Quick Reference

| Container Type | Image Setup | GPU | Use Case |
|---------------|-------------|-----|----------|
| CPU (debian_slim) | `.apt_install("ffmpeg")` | No | Batch processing, I/O-bound tasks |
| GPU (debian_slim) | `.apt_install("ffmpeg").pip_install("torch")` | Yes | ML inference, not NVENC |
| GPU (CUDA image) | `from_registry("nvidia/cuda:...")` | Yes | Full CUDA toolkit, NVENC possible |

| GPU Type | Price/Hour | NVENC | Best For |
|----------|-----------|-------|----------|
| T4 | ~$0.59 | Yes (Turing) | Inference + encoding |
| A10G | ~$1.10 | Yes (Ampere) | 4K encoding, ML |
| L40S | ~$1.95 | Yes (Ada) | Heavy ML + video |
| H100 | ~$4.25 | Yes (Hopper) | Training, overkill for video |

## When to Use This Skill

Use for **serverless video processing**:
- Batch transcoding that needs to scale to hundreds of containers
- Parallel video processing with Modal's map/starmap
- GPU-accelerated encoding (with limitations on NVENC)
- Cost-effective burst processing (pay only for execution time)
- Integration with ML models (Whisper, video analysis)

**Key decision**: Modal excels at parallel CPU workloads and ML inference on GPU. For pure hardware NVENC encoding, verify GPU capabilities first.

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# FFmpeg on Modal.com (2025)

Complete guide to running FFmpeg on Modal's serverless Python platform with CPU and GPU containers.

## Overview

Modal is a serverless platform for running Python code in the cloud with:
- **Sub-second cold starts** - Containers spin up in milliseconds
- **Elastic GPU capacity** - Access T4, A10G, L40S, H100 GPUs
- **Parallel processing** - Scale to thousands of containers instantly
- **Pay-per-use** - Billed by CPU cycle, not idle time

### Modal vs Traditional Cloud

| Feature | Modal | Traditional VMs |
|---------|-------|-----------------|
| Cold start | <1 second | Minutes |
| Scaling | Automatic to 1000s | Manual setup |
| Billing | Per execution | Per hour |
| GPU access | `gpu="any"` decorator | Complex provisioning