Modal — Serverless GPU Cloud for AI Workloads

What it is

Modal is a serverless cloud platform for running GPU workloads. You write Python functions, decorate them with @app.function(gpu='A100'), and Modal handles provisioning GPU instances, installing dependencies, and scaling. There is no infrastructure to manage: no Docker files, no Kubernetes, no cloud console. Modal supports A100, H100, and T4 GPUs for model inference, fine-tuning, batch processing, and web endpoints.

ML engineers, AI researchers, and developers who need GPU compute without infrastructure management benefit from Modal. It is particularly useful for workloads that are too bursty or infrequent to justify dedicated GPU instances.

How it saves time or tokens

Modal eliminates the hours spent setting up GPU infrastructure. No CUDA driver installation, no Docker image building, no autoscaling configuration. Cold start times are measured in seconds. You pay only for the GPU time you use (per-second billing), making it cost-effective for workloads that run for minutes or hours rather than continuously.

How to use

1. Install the Modal SDK and run modal setup for one-time authentication
2. Write a Python function decorated with @app.function(gpu='A100')
3. Run modal run script.py to execute on a cloud GPU

Example

import modal

app = modal.App('my-ai-app')

@app.function(gpu='A100')
def run_inference(prompt: str) -> str:
    from transformers import pipeline
    pipe = pipeline('text-generation', model='meta-llama/Llama-3-8B-Instruct')
    result = pipe(prompt, max_new_tokens=256)
    return result[0]['generated_text']

@app.local_entrypoint()
def main():
    output = run_inference.remote('Explain quantum computing.')
    print(output)

pip install modal
modal setup  # one-time auth
modal run inference.py

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Common pitfalls

• Cold starts add a few seconds on first invocation; use keep_warm=1 for latency-sensitive endpoints
• Large model downloads happen on every cold start unless you use Modal's volume mounts to cache weights
• GPU availability varies by type; H100s may have wait times during peak demand periods