Infrastructure & Cloud. GPU Inference
Serverless GPU Inference at Any Scale
Run AI inference on A100 and H100 GPUs without managing infrastructure. Sub-200ms cold starts, automatic scale-to-zero, and pay only for the compute you consume.
Overview
What is serverless GPU inference?
Dedicated GPU infrastructure is expensive, you pay for idle capacity even at 3am. Serverless GPU inference runs your models on demand: when a request arrives, a GPU spins up in under 200ms, executes the inference, returns the result, and scales back to zero. You pay only for the milliseconds of compute you actually use, with no reservation, no idle waste, and no infrastructure management.
What's included
Sub-200ms cold start
Optimised container snapshots and GPU memory pre-warming bring cold start times to under 200ms, fast enough for interactive applications.
A100 and H100 support
Run inference on the latest NVIDIA A100 and H100 GPUs. Choose the right GPU class for your model size and latency requirements.
Automatic scaling
The platform scales from zero to hundreds of concurrent GPU instances in seconds to absorb traffic spikes without configuration.
Model caching
Frequently used models are kept warm in memory to eliminate cold starts for high-traffic endpoints during peak hours.
Streaming inference
Support for token-streaming responses for LLM workloads so users see output in real time rather than waiting for full generation.
Multi-model endpoints
Deploy multiple model variants behind a single endpoint. Route traffic by model version, A/B test weight, or request parameter.
How it works
From setup to production
Package
Package your model as a container image using our base images optimised for GPU workloads and popular inference frameworks.
Deploy
Push the image and define GPU class, memory requirements, and scaling parameters. Deployment completes in under 3 minutes.
Invoke
Call the endpoint via REST or gRPC. The platform handles routing, cold starts, and load balancing transparently.
Monitor
Track latency, throughput, GPU utilisation, and cost per inference from the observability dashboard in real time.
FAQ
Common questions
Related
More from this service
Multi-Cloud
Distribute inference workloads across AWS, GCP, and Azure for cost and resilience.
Model Registry
Version, deploy, and monitor models deployed to GPU inference endpoints.
Cost Optimisation
Automatically right-size GPU instances and shift to spot capacity to reduce inference costs.
Get started
Run your first serverless GPU inference endpoint today
Talk to an expert and get a tailored implementation plan within 48 hours.