modal-serverless-gpu

Name: modal-serverless-gpu
Availability: InStock
Author: Nitish

Deploy Python-defined ML models on serverless GPUs for inference.

Updated Jun 26, 2026

Search Tags:#GPU acceleration #Python #ML deployment #serverless computing #model API

Ready to run

npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill modal-serverless-gpu-nitish-gitbit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: modal-serverless-gpu
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/modal
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill modal-serverless-gpu-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires modal, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenges of running GPU-intensive ML workloads without the need for infrastructure management, enabling on-demand GPU access and deployment of ML models as APIs.

Core Features & Use Cases

Serverless GPUs: Access T4, L4, A10G, L40S, A100, H100, H200, B200 GPUs on-demand.
Python-native: Define infrastructure in Python code, no YAML.
Auto-scaling: Scale to zero or scale up 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 Case: For a data scientist working on a time-sensitive project, this Skill allows for the rapid deployment of a GPU-accelerated model as an API, enabling real-time predictions without the need to manage infrastructure.

Quick Start

Use the modal-serverless-gpu skill to deploy a GPU-accelerated model as an API.

Frequently Asked Questions about modal-serverless-gpu

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

FAQPage Schema

How do I deploy a machine learning model as an API on serverless GPUs?▼

Deploying ML models as APIs on serverless GPUs requires Python-based infrastructure definition. You define your model and inference logic in Python, and the environment scales automatically from zero to over 100 GPUs instantly for on-demand processing.

What GPU types are available for on-demand ML inference?▼

Available serverless GPU types for ML inference include T4, L4, A10G, L40S, A100, H100, H200, and B200. These options allow you to match computational resources to your model's specific throughput and memory requirements.

Do I need to write YAML to configure serverless GPU infrastructure?▼

No, you do not need to write YAML to configure serverless GPU infrastructure. The deployment process is Python-native, meaning you define all infrastructure and scaling parameters directly within your Python code.

Can I use Modal for both real-time inference and batch processing?▼

Yes, you can use Modal for both real-time inference and batch processing. The platform deploys functions as REST APIs for real-time predictions while also supporting automated batch processing tasks on on-demand GPUs.

Why are cold starts a problem for serverless ML deployment and how are they handled?▼

Cold starts delay serverless ML deployment startup time. This is handled using a Rust-based infrastructure that enables sub-second container launches, alongside image layer caching for rapid iteration and zero-downtime web endpoint updates.

What is the best way to scale GPU-intensive ML workloads without infrastructure management?▼

The best way to scale GPU-intensive ML workloads without infrastructure management is using serverless computing. It provides auto-scaling capabilities that instantly adjust GPU allocation based on real-time inference demand, scaling up to 100+ GPUs or down to zero.