ffmpeg-modal-containers

Deploy FFmpeg in CPU or GPU containers on Modal for video transcoding.

51|10|Updated Oct 22, 2025

Search Tags:#serverless #ffmpeg #video processing #modal #transcoding #gpu encoding

Ready to run

npx skills add https://github.com/JosiahSiegel/claude-plugin-marketplace --skill ffmpeg-modal-containers

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ffmpeg-modal-containers
Source: https://github.com/JosiahSiegel/claude-plugin-marketplace/tree/main/plugins/ffmpeg-master/skills/ffmpeg-modal-containers
Command: npx skills add https://github.com/JosiahSiegel/claude-plugin-marketplace --skill ffmpeg-modal-containers

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the deployment and execution of FFmpeg for video processing tasks on Modal's serverless platform, enabling efficient, scalable, and cost-effective video transcoding and manipulation.

Core Features & Use Cases

Serverless FFmpeg Deployment: Easily set up FFmpeg in CPU or GPU containers on Modal.
GPU-Accelerated Encoding: Leverage NVIDIA GPUs for faster video encoding (NVENC support varies).
Parallel Processing: Utilize Modal's map and starmap for batch transcoding and frame-level processing.
Volume Integration: Handle large video files efficiently using Modal Volumes.
Cost Optimization: Choose between CPU and GPU based on task requirements and pricing.
Use Case: Transcode a batch of 1000 videos from MP4 to HLS format in parallel, leveraging GPU acceleration for faster processing and Modal Volumes to store the input and output files.

Quick Start

Use the ffmpeg-modal-containers skill to transcode a local video file named 'input.mp4' to 'output.mp4' using a CPU container on Modal.

Frequently Asked Questions about ffmpeg-modal-containers

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run FFmpeg video processing in a serverless environment?▼

Serverless video processing with FFmpeg can be achieved by deploying it in CPU or GPU containers on the Modal platform. This enables scalable video transcoding, encoding, and audio extraction without managing underlying infrastructure.

Can I use GPU acceleration for video transcoding on Modal?▼

Yes, GPU-accelerated video encoding is supported on Modal using NVIDIA GPUs. You can leverage NVENC for faster video transcoding by configuring a GPU container to optimize processing speed for your video workloads.

What is the best way to process large video files in serverless containers?▼

The best way to process large video files serverlessly is by integrating Modal Volumes. Modal Volumes handle large file storage efficiently, allowing FFmpeg to read inputs and write outputs directly without local storage bottlenecks.

How do I batch transcode multiple videos in parallel using serverless FFmpeg?▼

Batch transcoding of multiple videos is accomplished using Modal's distributed execution capabilities like `map` and `starmap`. These functions allow you to run parallel FFmpeg processing tasks across numerous videos simultaneously.

When should I choose CPU vs GPU containers for serverless video encoding?▼

Choosing between CPU and GPU containers for video encoding depends on task requirements and pricing. GPUs offer faster NVENC encoding for heavy workloads, while CPUs provide cost optimization for simpler video processing tasks.

Does serverless video transcoding support converting MP4 files to HLS format?▼

Yes, serverless video transcoding supports converting MP4 files to HLS format. You can deploy FFmpeg on Modal to transcode batches of MP4 videos into HLS streams using parallel processing and volume storage.