acceptance rate

Three parts, and most candidates finish one. The algebra fits on a line, the proof fits in three, and the part interviewers remember is the batch size at which the whole trick runs out of spare compute.

One option drafts four tokens for six percent of a target step but guesses each position blind; the other conditions every guess and costs nearly half a step. The answer prices both on Llama 3.3 70B and finds the batch size where the winner flips.

A one-layer drafter that beats an 8B model on acceptance sounds wrong until you ask what it is reading. The answer prices the EAGLE head on Llama 3.3 70B, explains the feature trick, and names the three changes in EAGLE-3.

Compute acceptance from counter snapshots, reject reset-contaminated windows, and handle EOS before inferring request-step counts. Then decide what a matched-load baseline must measure.

Code wants eight draft tokens and chat wants two, and a fixed gamma serves both badly. The answer simulates three policies on a mixed workload, finds the gain is 5% with a cheap drafter and 19% with an expensive one, then explains why batching takes most of it away.

The speedup formula has two inputs that vary per request and per moment, and the tail lives where both are bad. The answer computes when speculation turns into a slowdown, shows which requests own the p99, and picks a policy instead of an on/off switch.