decode

Derive the weight-only shortcut, then add KV traffic and feasible eight-GPU placement. A worked latency floor shows why batch size alone cannot establish the decode bottleneck.

Separate H200 bandwidth and capacity gains from measured speedup, then test realistic memory headroom and cost per useful completed workload.

Derive HBM bandwidth from interface width and pin rate, then connect it to a model that actually fits. Separate raw interface capacity from measured decode time.

Compare GPU memory, decode traffic and measured useful throughput. Work out a capacity ceiling without mistaking it for a performance forecast.

Use a timeline to establish launch overhead, then trace what graph replay changes. Stable buffers, page-table updates, capture-safe operations and batch buckets determine whether the serving engine can use it safely.

Follow a logical token through the block table into a physical KV page. Then combine split attention states correctly, including empty splits, and count lookup traffic at the layer and head the kernel actually processes.

Follow the weight bytes through a small decode batch and a long prefill. A scoped roofline calculation explains the difference without pretending to predict actual latency.

Compare a complete phase handoff with a tuned combined pool, then test the queueing reversal and qualified completions. Nominal link speed and fleet size alone cannot decide the split.

Thirty percent GPU utilization means kernels ran during about thirty percent of the sampled time. Find out what limits those kernels, what happens between them, and whether requests are meeting their latency targets.

Check the bound’s assumptions, locate the critical path, and quantify which optimization can actually improve one user’s decode latency.

Long prefills coincide with decode stalls. Compare a tuned combined pool with separate phases, derive transfer and pool capacity, and test whether the improvement survives connector failures.

Measure exposed launch and dispatch delay, actual graph coverage, capture memory and warmup. Batch size influences the result, but does not alone determine whether graphs improve the service.

Check model placement first, then derive a TP8 traffic floor and add context. Separate a single stream’s pace from an ideal bandwidth ratio.

Calculate FP8 savings by separating weights, KV cache and other step costs. Compare the same batch first, then check whether larger batches meet latency and quality targets.

Count weight and cache traffic on a specified B200 configuration, check finite capacity, and separate aggregate throughput from per-user pace. Worked bounds guide a benchmark rather than replace it.

Estimate a TP8 decode step from weights, cache reads and explicitly assumed collective times, then replace the serial worksheet with a measured critical path.

Derive the batch-295 shortcut, count the tensors it omits, and test whether the resulting kernel estimate is even inside the replica’s memory budget.

Use bandwidth per dollar as a first comparison, then check feasible memory, supported execution and complete service cost before ranking accelerators.

Define what a throughput claim counts, then compare its required arithmetic and memory traffic with a feasible deployment and matched latency targets.

Size both serving pools from workload-matched rates, then check memory, shared transfer paths and surviving capacity. Follow admission and buffer ownership through failure and scaling.