Latency-First Inference

Most AI infrastructure is tuned for shared, high-utilization compute. NeuroEtch instead studies a server architecture where the model path is narrow, direct, and mostly local to silicon.

Key points

Latency-first design reorders memory, routing, and economic decisions.

The benchmark comparison material in the repo is useful because it makes the tradeoffs explicit.

This is a serving-system thesis as much as a chip thesis.

Optimization target

Fast next token

Latency dominates over batch efficiency.

Cooling posture

Air-cooled reference

The reference thesis avoids extreme rack density.

Serving model

Model-dedicated

Infrastructure is designed around a known deployment target.

Different target, different hardware

When the system is optimized for a single model and a small number of request types, expensive generality becomes a liability. The architecture can be shaped around the exact path a token takes through the model.

That is why the repo repeatedly contrasts the program with GPU-style infrastructure instead of treating them as direct substitutes.

Practical implications

A latency-first system needs on-chip or near-chip data placement, stable scheduling, and a strict understanding of which claims are measured and which are theoretical.

Keep hot weights physically close to compute.

Reduce memory indirection and orchestration overhead.

Treat verification and benchmark provenance as first-class design work.