modal-serverless-gpu

You can deploy machine learning models to serverless GPUs by defining Python-native infrastructure requirements and entry points, which automatically provisions and scales NVIDIA GPU compute for inference or training workloads.

Running GPU-accelerated inference serverlessly removes infrastructure overhead by providing on-demand compute scaling across various NVIDIA GPU architectures, billed per second, scaling to zero when not in use.

Yes, you can deploy ML models as web endpoints that scale automatically based on traffic, allowing you to serve production-grade LLM inference APIs that provision high-performance GPUs like H100s or A100s dynamically.

No, you do not need to manually provision or maintain servers, though the serverless infrastructure interface does facilitate container image management and supports persistent storage integration for your Python-native workload definitions.

Serverless GPU execution supports on-demand compute scaling for training, inference, and batch processing tasks, allowing you to execute GPU-intensive machine learning workloads across multiple NVIDIA GPU architectures.

Serverless infrastructure for machine learning requires Python-native infrastructure definition and container image management, meaning workloads outside this Python ecosystem or requiring continuous dedicated servers may not be optimal for this deployment approach.