Compute, Silicon & System Integration

Choosing an accelerator commits you to one of four architectural families — merchant GPU, systolic TPU, hyperscaler XPU, inference ASIC — each with its own software stack, scale-up fabric, and lock-in horizon.

NVIDIA ships a new accelerator architecture yearly, each shifting the unit of purchase from chip to rack to pod; the rung you commit to sets your cooling, power, and refresh economics for years.

AMD's Instinct now matches NVIDIA on memory capacity and peak throughput, so the second-source question turns on whether your workload can absorb the ROCm-maturity tax and an unproven open scale-up fabric.

A hyperscaler XPU is silicon you rent into rather than own, so the decision hinges less on FLOPS-per-dollar than on living inside a compiler and supply chain the cloud controls.

A fixed-function ASIC beats a merchant GPU on cost per token only past a sustained-demand threshold; below it, the NRE and lead time buy a chip your roadmap has already obsoleted.

In 2026 HBM, not the GPU die, is the binding constraint on AI compute — sold out, dominating the bill of materials, and deciding how many accelerators ship.

How many accelerators the world can ship is set by how large an interposer a packaging house can yield — in 2026 the most-cited binding constraint on AI compute through the decade.

The GPU:CPU ratio and host-attach model decide whether your accelerators stay fed, and the agentic era has swung that composition hard back toward more CPU per GPU.

Buying an accelerator commits you to its software stack — CUDA, ROCm, XLA, or Neuron — and the switching cost shows up as a realized-MFU gap the datasheet never priced.

Each step down the precision ladder roughly doubles throughput and halves memory but spends accuracy headroom you cannot always recover — the question is how low a workload goes before its quality floor.

Score accelerators in cost-per-useful-token against whichever resource binds you — power or capital — then run an RFP that treats allocation, depreciation, and fleet heterogeneity as engineering variables.

On-package power delivery converts 48V to ~0.7V at 2,000+ amps within millimeters of the die; hold that rail flat or the accelerator throttles and the synchronized di/dt becomes a facility-scale transient.

The rack is now the unit you buy, ship, power, cool, cable, and certify as one object, so choosing its standard commits the floor, busbar, manifold, and cabling for a decade.

Where you enter the integration pipeline — DGX, HGX-OEM, ODM-direct, or OCP self-design — sets who owns burn-in, the acceptance gate, and the RMA, and the days between a powered shell and a producing cluster.

Time-to-goodput is set on the floor by how fast racks land and links light; pre-terminated cabling and off-line optics screening are the two levers that keep mis-cabling off the acceptance critical path.