Serverless changed how we run CPU code: write a function, deploy, scale to zero, pay per invocation. NVIDIA Cloud Functions (NVCF) does the same for GPU workloads. It's the closest thing to "serverless inference" that exists on NVIDIA's platform.
What NVCF is
NVCF is NVIDIA's serverless GPU function platform. You package a function (a model, a pre/post-processing step, an inference call), deploy it, and NVCF handles the scaling, the hardware, and the billing. You never see a server; you see a function that runs on a GPU when called.
Why it matters
- Scale to zero. No idle GPU cost when nobody's calling. The hardest part of running a GPU fleet is the idle time.
- Cold-start tradeoff. The GPU has to spin up, so first-call latency is higher. Fine for batch, painful for interactive.
- Elasticity. Burst to 100 GPUs for a loadtest, scale back to zero after. No capacity planning.
Serverless GPU is great for spiky, batch, or bursty workloads. It's wrong for steady-state interactive inference where cold starts hurt and you're better off with a warm fleet.
When it wins
- Batch inference - a nightly job that processes a million images. Scale to 100 GPUs, finish, scale to zero.
- Pre/post-processing - the small GPU tasks around a model call.
- Prototyping - try a model without committing to a fleet.
The takeaway
Serverless GPU is the right tool for spiky workloads and the wrong tool for steady-state ones. Know which you're running.
Sources
- NVCF repo: the serverless GPU function platform.
- NVIDIA Cloud Functions docs: the API, scaling model, and billing.
- NVIDIA developer blog: the NVCF announcements and use cases.
- NVIDIA aicr repo: the AI container registry that pairs with NVCF.