The premise.
You either buy or rent dedicated GPUs and accept that large parts of the hardware will sit idle most of the time, or you oversubscribe aggressively and accept unpredictable performance, noisy neighbours, and brittle workloads.
Our GPU utilisation technology is built on a simple observation: modern AI workloads rarely consume all aspects of a GPU at the same time. VRAM, compute, memory bandwidth, and interconnect are stressed differently depending on whether you're doing inference, fine-tuning, evaluation, or burst training.
Traditional infrastructure ignores this reality and prices GPUs as if they are a single, indivisible resource. They're not.
The core idea.
Instead of treating a GPU as "one job, one card", packet.ai allocates and schedules GPU resources based on what workloads actually consume in real time.
We track and manage GPU usage across multiple dimensions, not just whether a GPU is occupied, but how it is being used. Multiple compatible workloads can share the same physical hardware safely and predictably, without slicing the GPU into hard partitions or degrading performance.