Is speculative decoding worth enabling on a trillion-parameter mixture-of-experts model?

It helps most exactly where these models are weakest, which is single-user latency at low batch, and the published gains are larger than on dense models for a reason. The acceptance arithmetic, what it costs at high concurrency, and the measurement that decides.

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Speculative DecodingDecode is memory-bound: each step reads every weight to produce one token. Speculative decoding has a cheap draft propose several tokens, then verifies them all in one forward pass of the big model, so one weight read yields several tokens with output distribution unchanged. It wins 2x to 3x at small batch, breaks even near the ridge point where the GPU is already compute-bound, and lives or dies on the acceptance rate, which is what interviewers ask you to reason about.

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Expert Parallel and All-to-All BackendsA mixture-of-experts model can be split two ways and the choice changes everything. Tensor parallelism shards each expert across GPUs, which keeps every GPU busy and reads every expert's shard on every token. Expert parallelism gives whole experts to whole GPUs, which reads only the selected experts but requires an all-to-all to route tokens to them and back. The all-to-all is the cost, its backend is a configuration choice matched to the interconnect, and expert load imbalance is what actually limits the result.

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Continuous BatchingContinuous batching schedules at the granularity of a single decode step instead of a whole request, so a finished sequence's slot is refilled on the next iteration rather than when the longest request in the batch ends. It is the scheduling idea that turned LLM serving from a padded, half-idle GPU into one that stays full, and it decides how the engine's scheduler, memory manager and latency SLOs interact.

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Capacity Planning for Open-Weights FleetsPlanning a fleet for a sparse open-weights model works differently from planning one for a dense model, because memory follows total parameters and throughput follows active parameters, and those now differ by more than twenty times. The sizing goes in one direction only: from a traffic forecast to tokens per second, to replicas at a measured operating point, to GPUs, to racks and kilowatts. Doing it in the other direction, from an available GPU count, produces a fleet that fits the hardware rather than the demand.

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Next in this trackYour deployment is limited by KV cache rather than by compute. What are the options?Next in this trackYour p99 time to first token is four times p50. Find out why.Next in this trackYou have one week to get a newly released trillion-parameter model serving production traffic. Plan it.

FEDITOR'S NOTE

Scored on the expected-tokens formula and its dependence on acceptance, on why the gain is largest at low batch on sparse models, and on the cost at high concurrency.

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