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modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
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- MIT
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- 21.09.2026 г.
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Modal Serverless GPU
Comprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.
When to use Modal
Use Modal when:
- Running GPU-intensive ML workloads without managing infrastructure
- Deploying ML models as auto-scaling APIs
- Running batch processing jobs (training, inference, data processing)
- Need pay-per-second GPU pricing without idle costs
- Prototyping ML applications quickly
- Running scheduled jobs (cron-like workloads)
Key features:
- Serverless GPUs: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand
- Python-native: Define infrastructure in Python code, no YAML
- Auto-scaling: Scale to zero, scale 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 alternatives instead:
- RunPod: For longer-running pods with persistent state
- Lambda Labs: For reserved GPU instances
- SkyPilot: For multi-cloud orchestration and cost optimization
- Kubernetes: For complex multi-service architectures
Quick start
Installation
pip install modal
modal setup # Opens browser for authentication
Hello World with GPU
import modal
app = modal.App("hello-gpu")
@app.function(gpu="T4")
def gpu_info():
import subprocess
return subprocess.run(["nvidia-smi"], capture_output=True, text=True).stdout
@app.local_entrypoint()
def main():
print(gpu_info.remote())
Run: modal run hello_gpu.py
Basic inference endpoint
import modal
app = modal.App("text-generation")
image = modal.Image.debian_slim().pip_install("transformers", "torch", "accelerate")
@app.cls(gpu="A10G", image=image)
class TextGenerator:
@modal.enter()
def load_model(self):
from transformers import pipeline
self.pipe = pipeline("text-generation", model="gpt2", device=0)
@modal.method()
def generate(self, prompt: str) -> str:
return self.pipe(prompt, max_length=100)[0]["generated_text"]
@app.local_entrypoint()
def main():
print(TextGenerator().generate.remote("Hello, world"))