What is an AI inference platform and why does it matter for workload migration?

Q: What is an AI inference platform?

A: An AI inference platform is the combination of infrastructure and software used to run a trained AI model against new inputs and return outputs in production. This covers the compute, storage, networking and orchestration needed to serve a model reliably at the volume and speed a live application demands.

Q: How is AI inference different from AI training?

A: Training builds or refines a model using datasets and can require intensive compute over a defined training cycle. Inference runs that trained model against new data once it is deployed, either in real time or in batch processing. The two have different infrastructure profiles: training is generally optimised for throughput across compute-intensive jobs, while inference infrastructure has to meet production requirements around latency, throughput, concurrency and availability.

Q: What does an AI inference platform include?

A: A typical inference platform can combine accelerated compute, inference software and frameworks, storage, networking, orchestration and monitoring. For demanding AI models, that compute is often GPU-accelerated. NVIDIA technologies used for AI inference include Triton Inference Server, which standardises model deployment and execution across different frameworks and infrastructure, and NIM, which packages optimised inference capabilities into deployable microservices.

Q: Where does an AI inference platform fit within enterprise infrastructure?

A: It sits between the trained model and the applications or users consuming its outputs. Depending on latency requirements, data location and workload volume, that layer can run in public or private cloud, on-premises infrastructure, colocation or at the network edge closer to where data is generated. Pulsant's AI inferencing infrastructure offers GPU-accelerated IaaS alongside colocation options within its regional UK data centre network, allowing organisations to place inference closer to their data and users.

Q: Why does AI inference infrastructure need to be scalable?

A: Inference demand can vary with user volume, request patterns, model size and data volume once an application is live. Infrastructure that can't scale without a redesign becomes a bottleneck as usage grows, so scalable AI infrastructure means adding compute and connectivity incrementally, without re-architecting the deployment each time demand increases.

Q: Why does an AI inference platform matter when migrating workloads?

A: Migrating an AI inference workload isn't the same as moving a conventional virtual machine. Planning has to account for compute requirements and, where GPU acceleration is needed, GPU availability at the destination, model and framework compatibility, where the model, operational data and dependent systems sit, network latency between inference and the applications using it, and expected request volumes. Treating an AI workload like a standard VM migration risks landing it somewhere that can't run it at the performance the application needs.

Q: What should businesses assess before migrating an AI inference workload?

A: Check model and framework compatibility with the target environment, compute and GPU requirements, data dependencies and where that data needs to sit, network latency between inference and An AI inference platform is the infrastructure and software that runs a trained AI model against new data, generating outputs such as predictions, text or images for live applications. As more organisations move AI from pilot projects into live use, where and how inference runs becomes an infrastructure decision that affects compute, connectivity, data location and migration planning.

This FAQ explains what an AI inference platform is, where it fits in enterprise infrastructure, and what to assess before migrating an AI workload.

its consumers, security and compliance requirements, and whether the workload needs to stay close to users or data sources. Scaling requirements matter too: assess whether the target infrastructure can grow with demand without another migration down the line.

Q: What should businesses look for in an AI infrastructure provider?

A: Look for available GPU capacity, a clear path to scale compute as demand grows, strong connectivity between compute and data sources, and clear data sovereignty and security credentials. Support also matters where internal teams need help with infrastructure deployment, monitoring or scaling. Pulsant offers GPU-accelerated IaaS alongside colocation options for AI workloads within its regional UK data centre network, backed by 24/7 support from local data centre teams.

Planning an AI inference workload migration? Talk to the Pulsant team about GPU-accelerated infrastructure across our UK data centre network.