An inference chip is an accelerator optimized to run a trained neural network with low latency, high throughput, or low energy per request rather than to compute training gradients.

Serving is usually a data-movement problem. Large-model decode repeatedly reads weights and a growing key-value cache for each generated token. HBM bandwidth, on-chip SRAM, batching strategy, and cache management can matter more than peak arithmetic throughput.

Designs specialize by deployment. Data-center inference ASICs target efficient model serving at scale; edge chips emphasize INT8 and INT4 execution within tight power envelopes; GPUs retain flexibility across changing models and operators.

Software determines realized efficiency. Quantization, graph fusion, continuous batching, speculative decoding, and a mature compiler/runtime stack often separate a useful inference product from an impressive peak specification.