skills.rest
Mang30
Mang30COMMUNITY
Explore all skills by this author →
GitHub

serverless-modal

Run GPU training, inference, and batch workloads on Modal.

Updated May 29, 2026
Search Tags:#training#modal#inference#cost estimation#vllm#serverless gpu#auto scale-to-zero
One-click install
npx skills add https://github.com/Mang30/myskills --skill serverless-modal-mang30
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serverless-modal
Source: https://github.com/Mang30/myskills/tree/main/skills/serverless-modal
Command: npx skills add https://github.com/Mang30/myskills --skill serverless-modal-mang30

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the friction of provisioning, configuring, and managing remote GPU hardware by letting you run GPU workloads through Modal’s zero-setup serverless interface.

Core Features & Use Cases

  • Serverless GPU execution: Run training, fine-tuning, inference, and batch jobs without SSH, Docker, or port forwarding.
  • Auto scale-to-zero: Avoid paying for idle compute by running only while your job is active.
  • Deployment and performance patterns: Choose the right launcher style for one-shot experiments, persistent APIs, vLLM servers, LoRA fine-tuning, or multi-GPU distributed training.
  • Use Case: When you need to benchmark and decide between GPUs for a 7–8B BF16 inference workload, it guides you through cost/VRAM estimation and then generates the correct Modal launcher to run on the selected GPU tier.

Quick Start

Tell the assistant to run your workload on Modal by saying: "modal run training for my project with GPU auto-selection and show me a cost estimate first."

Frequently Asked Questions about serverless-modal

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

FAQPage Schema
How do I run GPU training and inference without setting up SSH or Docker?▼

Run GPU training and inference without SSH or Docker by using Modal's serverless interface to execute workloads directly. You define YAML metadata to generate launcher code that handles GPU selection and deploys remote execution environments automatically.

Can I use vLLM for high-performance serving on serverless GPUs?▼

Yes, vLLM high-performance serving is supported on serverless GPUs. The Skill generates Modal launcher code tailored for vLLM servers, allowing you to deploy persistent inference APIs without managing underlying infrastructure.

Does serverless GPU execution support auto scale-to-zero to avoid idle compute costs?▼

Yes, serverless GPU execution supports auto scale-to-zero to avoid paying for idle compute. Workloads run only while active, ensuring you incur costs solely during job execution and deployment periods.

What is the best way to estimate GPU VRAM and costs for a 7-8B BF16 inference workload?▼

Estimate GPU VRAM and costs for BF16 inference by using the Skill's built-in cost and VRAM estimation logic. It evaluates workload requirements and guides GPU selection before generating the deployment launcher.

How do I run multi-GPU distributed training or LoRA fine-tuning remotely?▼

Run multi-GPU distributed training and LoRA fine-tuning remotely by selecting the appropriate deployment pattern. The Skill generates Modal launcher code that configures the necessary multi-GPU environment for reproducible execution.

Related Skills

Explore More Software Engineering Skills→
Mang30Mang30

run-experiment

Launches ML training jobs on local, SSH, Vast.ai, or Modal GPUs with logging.

Community
Advanced
lightrain-alightrain-a

serverless-modal

Run GPU training and inference workloads on Modal with cost estimation.

Community
Advanced
Shallow-WShallow-W

serverless-modal

Enables GPU workloads on Modal from local code without SSH or Docker setup.

Community
Advanced
KarlinskySKarlinskyS

lambda-labs-gpu-cloud

Provision Lambda Labs GPU cloud instances for machine learning training and inference.

Community
Intermediate