Modal Cloud GPU — Training & Inference

Task: $ARGUMENTS

Overview

Modal is a serverless GPU cloud. Key advantages over SSH-based platforms (vast.ai, remote servers):

• Zero config: no SSH, no Docker, no port forwarding. Write Python → modal run → done.
• Auto scale-to-zero: billing stops the instant your code finishes. No idle instances.
• Local-first: run modal run from your laptop. Code, data, and results stay local; only the GPU function runs remotely.
• Reproducible environments: dependencies declared in code via modal.Image, not system-level packages. Treat the modal.Image chain as the RENDERED form of the declarative env spec in ../shared-references/compute-env-contract.md — same spec fields (base, ordered pip phases, env vars, smoke probes), same env:<name>@<specHash> ledger entry in .aris/compute/modal.md, same three-tier validation before a long run.

Best for: Users without a local GPU who need to debug CUDA code, run small-scale tests, or iterate quickly on experiments. The $5 free tier (no card) is enough for code debugging; $30 (with card) covers most small-scale experiment runs.

Trade-off: Modal costs more per GPU-hour than vast.ai or Lightning for some GPU tiers, but eliminates setup time and idle billing, often making it cheaper for short/medium workloads. For long training runs (>4 hours), consider vast.ai for lower $/hr.