One signal

Modelplane has published an open field guide to inference engineering for teams running AI models in production. It covers the practical work behind self-hosting, from sizing hardware for model weights and KV caches to spotting bottlenecks in prefill and decoding.

The guide also addresses quantisation, speculative decoding, observability, autoscaling and deployment rollouts. That makes it a useful map of the operational territory, not just a tour of model-serving terminology. For teams building their own AI infrastructure, the details between “the model works” and “the service runs reliably” are where the interesting work tends to gather.

Discuss: Which part of inference engineering causes more trouble in practice: hardware sizing, performance tuning or production operations?

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