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MDG™ - Model Defined GPUs is a model-first approach to GPU infrastructure where the requirements of the AI model determine how GPU resources are selected and shaped.

Instead of sizing GPUs in advance and forcing models to fit inside static constraints, MDG™ observes how a model actually behaves and aligns GPU resources accordingly. Memory, compute, and execution characteristics are matched to real workload needs rather than assumptions.

This approach reduces over-provisioning, minimizes instability caused by under-sizing, and removes the trial-and-error cycles that slow models from reaching production.

With MDG™, the model defines the GPU, not the other way around. is a model-first approach to GPU infrastructure where the requirements of the AI model determine how GPU resources are selected and shaped.

Instead of sizing GPUs in advance and forcing models to fit inside static constraints, MDG™ observes how a model actually behaves and aligns GPU resources accordingly. Memory, compute, and execution characteristics are matched to real workload needs rather than assumptions.

This approach reduces over-provisioning, minimizes instability caused by under-sizing, and removes the trial-and-error cycles that slow models from reaching production.

With MDG™, the model defines the GPU, not the other way around.

MAG™ – Model Adaptive GPUs extend the MDG™ concept by enabling GPU resources to adjust dynamically as model behavior changes over time.

AI workloads are not static. Batch sizes vary, token lengths fluctuate, and inference and training place different demands on the system. MAG™ allows GPU allocations to adapt as those conditions shift, without manual intervention or reconfiguration.

Rather than locking models into fixed GPU profiles, MAG™ helps ensure that resources evolve alongside the workload, maintaining stability and efficiency as demand changes.

MAG™ turns GPU infrastructure into a responsive system instead of a fixed constraint.

Intelligent Packing™ describes how Rapt places and orchestrates multiple AI workloads across available GPU resources with precision.

Instead of competing for shared cores, context switching inefficiently, or interfering with one another, models are packed in a way that avoids contention and aligns each workload with the specific resources it needs. Every model receives the right amount of GPU capacity, at the right time, without excess or collision.

This results in higher utilization, more predictable performance, and better overall stability across shared infrastructure.

Intelligent Packing™ ensures models run cleanly, efficiently, and without friction.