Allocating a whole graphics processing unit, or GPU, to an AI model is one deployment choice. Dividing that accelerator into smaller resources is another. The useful question is not simply whether sharing is possible, but whether each model gets enough capacity to meet its requirements.
A recent announcement provides a concrete example. In a post published on November 6, 2026, AWS reported that SageMaker Inference now supports NVIDIA Multi-Instance GPU, or MIG. According to the announcement, MIG divides one GPU into hardware-isolated partitions. Teams can run a different model in each partition or use a right-sized fraction of a newer accelerator.
That is the reported development. The broader engineering lesson is an analysis: resource allocation deserves its own evaluation, separate from choosing the model.
What partitioning changes
Inference means running a trained model to produce an output, such as a prediction or generated response. A model endpoint is the service through which an application requests that output.
With partitioning, the allocation decision becomes more granular than assigning an entire GPU. Think of dividing a workspace into separate rooms: the arrangement changes how space is assigned, but it does not prove that every occupant has enough room.
AWS says its implementation uses two optional API fields to place one model per partition. That describes a configuration mechanism—not evidence that any particular workload will become faster or cheaper. A team needs workload-specific measurements before making either claim.
A practical evaluation checklist
The following steps are recommendations, not results reported by the announcement.
First, check memory fit. Measure the workload's memory requirements under representative requests, including the largest inputs you intend to support. Treat a successful startup as the beginning of testing, not proof that the allocation is adequate.
Second, test response time under realistic load. Compare a candidate partition with a whole-GPU baseline using the same model, inputs and service target. Examine slower responses as well as averages: an acceptable average can hide requests that miss your target.
Third, examine operational effort. Ask how the team will assign models, monitor capacity, handle failed deployments and change allocations as demand shifts. A finer-grained allocation is useful only if the team can operate it reliably.
Fourth, compare total cost. Include hosting charges and engineering effort rather than assuming that a smaller allocation automatically produces a cheaper service. Keep the comparison tied to requests completed within your quality and response-time requirements.
Isolation is not a complete assurance
The source describes hardware-isolated partitions. Do not extend that statement into a guarantee about application permissions, data handling or end-to-end security. Those controls still need their own review.
Start with one candidate workload and explicit acceptance criteria. Keep the whole-GPU option as a baseline, and adopt partitioning only if measurements justify the change. The goal is not to divide every accelerator; it is to match resources to the service you actually need.