modal-serverless-gpu

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"))

Core concepts

Key components

Component	Purpose
App	Container for functions and resources
Function	Serverless function with compute specs
Cls	Class-based functions with lifecycle hooks
Image	Container image definition
Volume	Persistent storage for models/data
Secret	Secure credential storage

Execution modes

Command	Description
modal run script.py	Execute and exit
modal serve script.py	Development with live reload
modal deploy script.py	Persistent cloud deployment

GPU configuration

Available GPUs

GPU	VRAM	Best For
T4	16GB	Budget inference, small models
L4	24GB	Inference, Ada Lovelace arch
A10G	24GB	Training/inference, 3.3x faster than T4
L40S	48GB	Recommended for inference (best cost/perf)
A100-40GB	40GB	Large model training
A100-80GB	80GB	Very large models
H100	80GB	Fastest, FP8 + Transformer Engine
H200	141GB	Auto-upgrade from H100, 4.8TB/s bandwidth
B200	Latest	Blackwell architecture

GPU specification patterns

# Single GPU
@app.function(gpu="A100")

# Specific memory variant
@app.function(gpu="A100-80GB")

# Multiple GPUs (up to 8)
@app.function(gpu="H100:4")

# GPU with fallbacks
@app.function(gpu=["H100", "A100", "L40S"])

# Any available GPU
@app.function(gpu="any")

Container images

# Basic image with pip
image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch==2.1.0", "transformers==4.36.0", "accelerate"
)

# From CUDA base
image = modal.Image.from_registry(
    "nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04",
    add_python="3.11"
).pip_install("torch", "transformers")

# With system packages
image = modal.Image.debian_slim().apt_install("git", "ffmpeg").pip_install("whisper")

Persistent storage

volume = modal.Volume.from_name("model-cache", create_if_missing=True)

@app.function(gpu="A10G", volumes={"/models": volume})
def load_model():
    import os
    model_path = "/models/llama-7b"
    if not os.path.exists(model_path):
        model = download_model()
        model.save_pretrained(model_path)
        volume.commit()  # Persist changes
    return load_from_path(model_path)

Web endpoints

FastAPI endpoint decorator

@app.function()
@modal.fastapi_endpoint(method="POST")
def predict(text: str) -> dict:
    return {"result": model.predict(text)}

Full ASGI app

from fastapi import FastAPI
web_app = FastAPI()

@web_app.post("/predict")
async def predict(text: str):
    return {"result": await model.predict.remote.aio(text)}

@app.function()
@modal.asgi_app()
def fastapi_app():
    return web_app

Web endpoint types

Decorator	Use Case
@modal.fastapi_endpoint()	Simple function → API
@modal.asgi_app()	Full FastAPI/Starlette apps
@modal.wsgi_app()	Django/Flask apps
@modal.web_server(port)	Arbitrary HTTP servers

Dynamic batching

@app.function()
@modal.batched(max_batch_size=32, wait_ms=100)
async def batch_predict(inputs: list[str]) -> list[dict]:
    # Inputs automatically batched
    return model.batch_predict(inputs)

Secrets management

# Create secret
modal secret create huggingface HF_TOKEN=hf_xxx

@app.function(secrets=[modal.Secret.from_name("huggingface")])
def download_model():
    import os
    token = os.environ["HF_TOKEN"]

Scheduling

@app.function(schedule=modal.Cron("0 0 * * *"))  # Daily midnight
def daily_job():
    pass

@app.function(schedule=modal.Period(hours=1))
def hourly_job():
    pass

Performance optimization

Cold start mitigation

@app.function(
    container_idle_timeout=300,  # Keep warm 5 min
    allow_concurrent_inputs=10,  # Handle concurrent requests
)
def inference():
    pass

Model loading best practices

@app.cls(gpu="A100")
class Model:
    @modal.enter()  # Run once at container start
    def load(self):
        self.model = load_model()  # Load during warm-up

    @modal.method()
    def predict(self, x):
        return self.model(x)

Parallel processing

@app.function()
def process_item(item):
    return expensive_computation(item)

@app.function()
def run_parallel():
    items = list(range(1000))
    # Fan out to parallel containers
    results = list(process_item.map(items))
    return results

Common configuration

@app.function(
    gpu="A100",
    memory=32768,              # 32GB RAM
    cpu=4,                     # 4 CPU cores
    timeout=3600,              # 1 hour max
    container_idle_timeout=120,# Keep warm 2 min
    retries=3,                 # Retry on failure
    concurrency_limit=10,      # Max concurrent containers
)
def my_function():
    pass

Debugging

# Test locally
if __name__ == "__main__":
    result = my_function.local()

# View logs
# modal app logs my-app

Common issues

Issue	Solution
Cold start latency	Increase container_idle_timeout, use @modal.enter()
GPU OOM	Use larger GPU (A100-80GB), enable gradient checkpointing
Image build fails	Pin dependency versions, check CUDA compatibility
Timeout errors	Increase timeout, add checkpointing

References

• Advanced Usage - Multi-GPU, distributed training, cost optimization
• Troubleshooting - Common issues and solutions

Resources

• Documentation: https://modal.com/docs
• Examples: https://github.com/modal-labs/modal-examples
• Pricing: https://modal.com/pricing
• Discord: https://discord.gg/modal

Agent operating procedure

1. Check the environment. Confirm hardware, framework and server versions, model format, and expected load.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Serve or log a single request or run end to end before scaling.
4. Execute the full task using the instructions and references above.
5. Validate the result. Measure latency, throughput and output correctness against a reference; check resource usage and costs.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
The server fails to start or OOMs	Check model size versus memory, quantization and parallelism settings.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Do not expose services or credentials publicly; confirm cloud costs before provisioning.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• modal: Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs.
• skypilot-multi-cloud-orchestration: Multi-cloud orchestration for ML workloads with automatic cost optimization.
• lambda-labs-gpu-cloud: Reserved and on-demand GPU cloud instances for ML training and inference.