throughput

Compare useful parallel work, data movement and reuse. A synthetic CPU/GPU crossover shows why a faster kernel can still lose the end-to-end request.

Budget the full megawatt, fit whole servers, and compare useful training throughput under measured power limits. A worked cap example shows when more GPUs help.

Compute MFU from a declared global token count and time interval. Check the accounting before judging the percentage or choosing an optimization.

One request finishes, another keeps writing, and a third waits outside. Follow the same workload through two schedules to see what continuous batching improves, and what it cannot promise.

Preserve document identity through packed prompts and completion reordering. Derive rounded cache reservations, then measure feasible wave sizes against the API contract.

Separate device throughput bounds from measured service curves, then choose a feasible operating point using latency, quality and goodput.

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.

Compare committed output per complete draft-and-verify cycle with ordinary decoding. Derive the acceptance-length model, separate it from engine counters, and test latency, goodput and memory across the actual workload.

Apply the project’s offline tuning guidance in context, derive the memory tradeoff, and use workload-specific acceptance criteria instead of universal queue and headroom targets.

Constrained decoding restricts valid continuations; it does not guarantee completion or correct values. Define success and error contracts, validate supported schemas, and measure compilation, masking and queueing under the actual workload.

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.

A measured run shows where the time goes in a small batch job, then why every result parsed but none was deliverable. The answer turns on startup cost, co-tenant latency and a per-item contract.

Compute cost from a matched bill and token count. Show when an activity factor belongs in the denominator and when it would double-count idle time.

Translate active streams into replicas using growth reservations and matched latency tests. Show peak and failure headroom separately, with no invented benchmark.

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.

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

Plan a reproducible tokenization build: measure CPU demand, choose a safe token dtype, publish complete shards and distinguish token lookup from training resume.

Track request admission through a concrete page-reservation example, then connect scheduling, growth and stream cleanup to measured service limits.

A 128,000-ID vocabulary breaks the 16-bit storage estimate. Follow the bytes from raw ingestion to 60 TB of token payload, then design deduplication, manifests and a restartable loader.

Compare the output of a speculative round with drafting and verification cost. Work the break-even condition, then design a measured rollout with memory and correctness checks.