Use case

Reducing inference energy per token with serving software

How serving stacks, quantisation and batching change energy per generated token, and what to measure before choosing one.

5 tools in this scenario · verified 2026-10-27

Inference has become the larger share of compute for most teams shipping language models, and the serving stack is the lever that can move energy per token without retraining anything. The options divide into four layers: the engine that schedules requests and manages KV cache, the quantised weight formats it loads, the batching and speculative decoding it applies, and the multi-GPU parallelism strategy underneath. A change in any layer alters both throughput and the energy attributed to each request, and the trade-offs are not additive. A stack that doubles throughput can still increase total energy if it keeps GPUs busy on traffic that would otherwise have idled, so the metric that matters is energy per useful output at a stated latency target, not tokens per second in a benchmark. Measuring it means holding the model, the workload mix and the latency SLO fixed while swapping one layer at a time, then reading power from the same source throughout. The criteria below set up that comparison without depending on any vendor's published benchmark.

What to evaluate

1
Measured energy per token, not throughput alone
Require energy per generated token or per request at a defined latency target. Throughput figures taken at maximum batch size rarely describe the production mix of short and long requests.
2
Quantisation support and accuracy risk
Check which weight formats the engine loads natively and whether the accuracy impact has been evaluated on your own task. A format that halves memory may not reduce energy per token if it needs more scheduling overhead.
3
KV cache and memory pressure
Long-context traffic is limited by KV cache more than by compute. Ask how the stack pages or evicts cache and what that does to recomputation and energy on repeated prompts.
4
Parallelism strategy across GPUs
Tensor, pipeline and expert parallelism scale differently and change both latency and idle power. Confirm which strategies the stack supports for your model size and interconnect.
5
Operational cost of the swap
Model conversion, driver versions, hardware support and rollback paths decide whether a faster engine is deployable. Cost the migration effort alongside the energy saving.