modal-serverless-gpu

You can deploy machine learning models as REST endpoints that auto-scale on serverless GPUs by defining Python-native infrastructure, allowing workloads to scale to zero when idle and handle traffic spikes automatically.

Running GPU-accelerated batch processing without server maintenance involves executing workloads on-demand using high-performance hardware like H100s and A100s, eliminating idle costs and infrastructure overhead.

Multi-GPU orchestration for serverless machine learning inference coordinates high-performance hardware to execute parallel workloads, facilitating model training and web service hosting with sub-second cold starts.

Yes, you can use Python-native infrastructure definition to manage persistent storage and secret management for ML workloads, securely handling credentials and maintaining state across serverless GPU executions.

Serverless GPU execution supports sub-second cold starts for production inference APIs, ensuring high availability and rapid response times when deploying large language models or other ML workloads.

Choosing serverless GPUs over traditional cloud instances eliminates idle costs and server maintenance overhead, providing auto-scaling infrastructure that matches compute supply to inference traffic demand.