evaluation

Quantize a sixteen-value block, count the scale bytes, and use paired quality tests to decide which tensors need higher precision.

Separate valid 2:4 structure, packed representation and sparse dispatch from model quality and the fraction of request time that can improve.

Test the vendor claim on your workload, including quality, latency, scaling and operating costs. A lower peak or lower price alone does not settle it.

Version teacher outputs, separate learning from evaluation, and catch a release whose domain score improves while a protected capability regresses.

Compare actual encoded tensors, per-rank runtime fit, supported kernels and held-out task quality. Four-bit weight-only kernels can accelerate wider arithmetic, and smaller weights need not reduce the GPU count.

A minor revision can change weights, encoding or response behavior. Compare pinned deployment bundles, evaluate task slices, monitor a representative canary and keep enough capacity to restore the old service.

Define whether half the footprint means checkpoint bytes or runtime memory. Choose a supported recipe, measure which tensors it changes, separate calibration from held-out evaluation, and validate quality and serving cost.

Test the complete serving bundle with contract fixtures, representative tasks and load-aware outcomes. Preserve paired results, calibrate the scorer and distinguish a relative improvement from meeting an absolute product requirement.

Screen exact checkpoints for hard requirements, then compare deployable candidates on held-out product tasks, service limits and total cost. Smaller artifacts, leaderboard scores and token rates each answer only part of the decision.

Turn a benchmark number into a bill that survives real utilization, then write the serving contract the gateway enforces and the evaluation the tuned configuration still owes.