Choosing inference hardware from constraints

Hardware selection should follow workload constraints. A useful first pass lists model size, context length, expected request shape, batching tolerance, memory pressure, power envelope, software stack, and operational ownership.

Measure what constrains the design

Throughput, latency, and memory behavior change with prompt shape, generation length, batching, quantization, and runtime implementation. Measurements should be dated and tied to the exact workload they represent.

Include facility constraints

Accelerators sit inside broader infrastructure. Network topology, storage access, power delivery, cooling, maintenance windows, and remote-management requirements can decide whether a design is practical.

Treat cost as a system property

Hardware price is only one input. Utilization, support burden, power, integration time, redundancy, and replacement planning all matter when comparing deployment options.