serverless-modal

Serverless GPU execution on Modal runs training and inference workloads without SSH or Docker setup. You execute tasks via generated launchers, and billing automatically stops when your workload finishes to prevent idle-instance costs.

Yes, mandatory cost estimation occurs before any execution. The workflow compares GPU options by estimated $/hr based on your specific model parameters, ensuring you select an appropriate GPU and understand the compute cost before generating a launcher.

Generating a ready-to-run Modal launcher for web/API services deploys persistent inference endpoints. This supports high-performance vLLM inference and scales to zero automatically, eliminating idle-instance costs while maintaining endpoint availability.

Yes, launcher generation supports multi-GPU distributed training and LoRA fine-tuning patterns. It also handles one-shot experiments, batch processing, and standard model training by generating the appropriate Modal execution configuration automatically.

Request GPU cloud execution and the workflow estimates costs, compares GPU options by estimated $/hr, and generates a ready-to-run Modal launcher. This allows you to execute the benchmark and produce results without manual environment setup overhead.