modal-serverless-gpu

You can deploy machine learning models as APIs by defining Python-native infrastructure that automatically scales from zero. This approach provides serverless GPU access for ML workloads without the overhead of managing cloud servers.

Yes, you can access serverless NVIDIA GPUs like T4, A100, and H100 on a pay-per-second basis. This allows you to run scalable batch inference and training jobs on-demand without upfront hardware costs.

Serverless GPU auto-scaling works by provisioning multiple GPUs like A100s for training, then automatically scaling resources down to zero when the job completes. This ensures you only pay for active compute time.

The cold start time for serverless ML deployments is sub-second. This is achieved using Rust-based infrastructure, enabling rapid scaling and immediate availability for on-demand GPU compute.

Yes, you need the Modal dependency to use this serverless GPU cloud for ML. It enables Python-native infrastructure definition, allowing you to programmatically define and manage your scalable GPU resources.

The best way to run scalable batch jobs without owning GPU hardware is using serverless GPU compute. It enables on-demand access to various NVIDIA GPUs, auto-scaling from zero, and sub-second cold starts for efficient batch processing.