GPU inference specialists for your dedicated team.

Where GPU inference fits in a long-term product team.

Serving one request at a time wastes most of a GPU. The hardware is built for parallelism, and a single small input leaves the device mostly idle, which is why an unbatched service can reach only a small fraction of what the same hardware delivers batched.

Cost per prediction is the number that matters and is rarely calculated. A GPU instance billed hourly serving a low request rate can cost more per prediction than CPU inference would, and for many models the CPU option is entirely adequate.

What your dedicated team does with GPU inference.

Autoscaling GPU workloads is harder than autoscaling web services. Instances take minutes to become available and models take time to load, so scaling reacts far too slowly for a traffic spike.

Designing for that, with warm pools, predictive scaling and queueing, is platform work alongside a dedicated cloud and DevOps team.

Common GPU inference use cases.

• Batching that uses the hardware Requests grouped, because unbatched serving wastes most of the device.
• Cost per prediction calculated GPU compared carefully against CPU, since low volume often favours CPU.
• Scaling that anticipates demand Warm capacity, because instance start and model load are too slow to react.

Adding GPU inference skills to your team.

Inference infrastructure is assigned across AI and platform capacity, with cost per prediction established before hardware is chosen.

You can add GPU inference capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.