CPU, GPU or Neural Engine? Choosing the Right Compute Unit for Inference
Published: August 29, 2026 · Read Time: 6 min read · Category: Machine Learning
Author: Abhishek Shivakumar (Systems & Audio Engineering)
Short interactive workloads change the answer to where a model should run. We compare memory movement, dispatch overhead, latency and sustained performance.
Throughput Is Not the Whole Workload
Inference benchmarks usually report tokens per second, but an interactive application has a different shape. It may process a short prompt, emit one token at a time, wait for a user, and then run again. Kernel dispatch, memory transfers and wake-up latency can matter more than peak arithmetic throughput.
For small autoregressive models, decode is often a memory movement problem. The machine repeatedly reads weights to produce a small amount of new information. A fast accelerator is useful only if the work is large enough to repay the cost of getting there.
CPU for Small Streaming Decode
The CPU is often the simplest path for a compact model. Its caches are close to the cores, the runtime can keep ownership of the state, and a token callback does not need to cross an API boundary for every step.
This is the reason Razor is CPU-first for small ARM models. The goal is not to declare the CPU universally faster; it is to keep the complete per-token path short and predictable.
GPU for Batched Work
GPUs become attractive when there is enough parallel work: prompt prefill, multiple requests, larger matrices or many simultaneous streams. Their throughput can dominate once dispatch overhead is amortised across a batch.
The same model can therefore have two sensible execution paths. A batched prefill kernel and a low-overhead decode loop are not competing philosophies; they solve different phases of inference.
Neural Engines and Fixed Pipelines
Neural accelerators are compelling when the model graph and operators fit the supported compilation path. They can deliver excellent performance per watt, but a product still has to account for conversion, supported operations, memory placement, startup time and fallback behaviour.
Measure the interactionRecord time to first token, inter-token latency, sustained throughput, memory footprint and temperature. A single peak tokens-per-second number cannot choose an architecture for you.
Choose Against the User's Wait
For dictation, first useful text matters. For a background batch job, total throughput matters. For a mobile assistant, sustained performance and battery matter. The right compute unit is the one that satisfies the actual wait the user experiences. It should not be picked for the highest chart number alone.