sampling

A row of logits is half a megabyte, so the kernel is not bandwidth-bound and the cost is in how many passes you make over it. Why a full sort does far more work than the question asks, the two shapes that fit small and large k, and the four-pass radix select with a reference that matches a sort exactly.

Temperature zero removes the sampling randomness and leaves the floating-point kind. The batch your request lands in changes the reduction order, the logits move in the last bits, and a near-tie flips a token. The fix has a cost.

Model evaluations answer whether the weights are good and deployment evaluations answer whether your configuration serves them correctly, which is a different and more common failure. What to sample, why category averages hide the regressions that matter, and the reference that makes a result mean something.

GPU utilization at 100% tells you nothing, and a trace per token would cost more than the tokens. The span layout for one request, the GPU counters that read headroom, the cardinality budget that keeps the metrics store alive, and cost attribution that charges tenants for the batch share they used.

Four lines of arithmetic with three ways to get it wrong: dividing by a temperature of zero, exponentiating without subtracting the maximum, and applying the filters in an order that changes the result. The implementation, the verification against the analytic distribution, and the overflow that is one logit away.