AI Inference

Opening — Why this matters now Large Language Models are no longer compute-bound at training time. They are inference-bound at deployment time. The last year has made this painfully clear. Frontier reasoning models increasingly win benchmarks not by being smarter, but by thinking more: longer chains-of-thought, more samples, more retries, more votes. The result is an arms race in test-time scaling—512 samples here, best-of-20 there—where accuracy inches upward while token bills explode. ...

Opening — Why this matters now Speculative decoding has quietly become one of the most important efficiency tricks in large language model inference. It promises something deceptively simple: generate multiple tokens ahead of time with a cheap draft model, then let the expensive model verify them in parallel. Fewer forward passes, lower latency, higher throughput. ...

Opening — Why this matters now LLM inference has quietly become the dominant cost center of modern AI systems. Training grabs headlines; inference drains budgets. As models scale into the tens of billions of parameters, every additional forward pass hurts — financially and operationally. Speculative decoding promised relief by letting small models run ahead and big models merely verify. But verification, ironically, became the bottleneck. ...