GPU Inference
Our infrastructure layer supports GPU-backed inference where local or open-weight model execution requires accelerator memory and parallel compute that general-purpose CPU execution cannot provide economically at the required latency or throughput.
Capacity is not GPU count
Production inference depends on the interaction between:
- Model size
- Memory
- Throughput
- Concurrency
- Batch size
- Quantization
- Latency
Memory envelope
Model weights must fit within the available memory envelope together with runtime overhead and the state required during inference. Longer context, larger batches, and more concurrent requests can increase memory consumption even when the model itself is unchanged.
A configuration that successfully loads a model is not automatically a production-capable configuration.
- Model weights
- Runtime overhead
- Context / cache state
- Concurrent workload
The infrastructure needs headroom for the workload, not only for model initialization.
Throughput versus latency
GPU utilization is also a scheduling problem. Batching can increase throughput by processing work together, while interactive requests may require lower queueing delay. Higher concurrency can improve hardware utilization until memory pressure or contention begins degrading latency.
The correct operating point depends on whether the workload is interactive, asynchronous, high-volume, or latency-insensitive.
Quantization and model selection
Quantization can reduce the memory required to serve a model and change which models fit on a given accelerator. A smaller model can also provide better throughput and concurrency where the larger model does not materially improve workload quality. Those are evaluated together.
We do not optimize GPU infrastructure for the largest model that can technically run. We optimize for the quality, throughput, latency, and cost profile required by the workloads routed to that hardware.
Multi-GPU does not automatically mean pooled memory
Physical accelerator capacity also has topology constraints. Adding another GPU does not necessarily create one transparent memory pool for an arbitrary model. The runtime and model architecture must support the chosen form of parallelism, and interconnect bandwidth can become part of the performance envelope.
Depending on the model and serving stack, execution can involve tensor, pipeline, or other forms of partitioning, each with different communication and scheduling costs. This is why raw aggregate VRAM is an incomplete infrastructure metric. A four-GPU system with sufficient total memory can still perform poorly if the model partition, inter-GPU communication, batch shape, or runtime is mismatched to the workload.
We therefore evaluate the actual serving topology rather than inferring production capacity from accelerator specifications alone.