Training & fine-tuning
Long-running training connects model fit, collective communication, data delivery and recoverability. Fine-tuning may have a very different memory and duration profile from pretraining.
ExploreHome / WORKLOADS
WORKLOADS
Start with the
useful outcome.
GPU decisions change with the application, the quality requirement and the way work moves through a system.
EXPLORE
Four starting points.
Choose a workflow, then identify the evidence a representative pilot needs.
Inference & agents
Serving requires an explicit service envelope. Agentic applications add tool calls, variable context and multiple model passes; a single device throughput claim cannot describe the entire workflow.
ExploreHPC & scientific computing
Simulation and research workloads may depend on numerical precision, mature libraries and tightly coupled communication. Match the actual solver and dataset to the infrastructure.
ExploreRendering & visual computing
GPU rendering is a production workflow, with scene memory, engine compatibility, licensing and delivery deadlines. Start with the assets and acceptance standard, then test the queue.
ExploreWORKLOAD TO SYSTEM
Use the same decision sequence.
01
Workload & quality
Model, dataset, precision, latency and accepted output.
02
Compute & memory
Working set, accelerator architecture and parallelism.
03
Fabric & data
Scale-up links, scale-out networking, storage and checkpoints.
04
Power & facilities
Operating draw, cooling, site readiness and delivery sequence.
05
Operations & control
Scheduling, identities, observability, recovery and cost ownership.