bandwidth

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

Localize a slow fabric rail with matched tests, path comparisons and counter deltas. Separate an independent-bandwidth sum from collective completion time.

Diagnose a multi-node regression by comparing host and GPU transfer paths, active registration mechanisms and the tested software stack.

Build a storage budget that includes retained checkpoints, the next save, overlapping datasets and failure protection. Then check whether the same tier can meet write and restore deadlines.

Map a non-divisible array onto blocks, guard the final 189 threads, and separate kernel traffic from host transfers without inventing measured timings.

Count warp sectors for contiguous, strided and masked accesses, then use cache and timing evidence to decide whether changing the mapping helps.

Follow intermediate tensors through separate and fused stages, calculate the traffic and whole-block bounds, and check spills and numerical behavior.

Build a reduction with a checked tail, one partial result per block and a final combine. Count the input bytes, then investigate the instructions, synchronization and numerical behavior that a bandwidth estimate leaves out.

Trace how a shared-memory tile turns coalesced input rows into coalesced output rows. Then separate global sector footprints, shared-bank conflicts and measured bandwidth, including the cost of handling edge tiles.

Trace a chunk, derive both traffic phases, and state the endpoint model behind bandwidth optimality. A worked example shows why nearly constant bytes do not imply constant completion time.

Trace a model startup through storage, runtime setup and device loading. Transfer floors and dependencies determine whether a minute is plausible.

A conversation's cache is leaving GPU memory. Work out whether to save it, reload it later, or recompute, including transfer queues and the chance that nobody returns.

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

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

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.

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

Distinguish GPU-memory capability, registration and selected transport. Compare direct and staged paths without relying on a half-speed rule or one log substring.