modal-serverless-gpu

Deploy machine learning models as auto-scaling APIs on serverless GPUs by defining your infrastructure in Python. This approach allows you to serve inference workloads without managing underlying servers, utilizing sub-second cold starts and container caching for rapid scaling.

You can run batch inference jobs on cloud GPUs using a pay-per-second pricing model. This serverless infrastructure executes your workload on-demand and automatically scales down when the job finishes, ensuring you only pay for active compute time.

Available GPUs for on-demand machine learning workloads include T4, A10G, A100, and H100 types. This variety allows you to select the appropriate hardware acceleration for tasks ranging from model fine-tuning to large-scale batch training.

Fine-tune large language models without local GPUs by spinning up remote serverless compute instantly. You can execute your Python training script on powerful cloud hardware and shut it down immediately upon completion, paying only for the compute time used.

Defining cloud infrastructure in Python works seamlessly for serverless GPU deployment. This method enables developers to programmatically configure environments, execute ML workloads, and manage container caching without needing separate configuration files.

Limitations of using serverless GPUs for ML training include potential cold start overhead, although sub-second cold starts and container caching mitigate this. It is best suited for intermittent workloads rather than persistent, multi-week training runs.