Research · Inference

Inference optimisation for self-hosted LLMs

Serve more users on the same GPUs through quantisation, batching and speculative decoding, without measurable quality loss on your evals.

2–5×more throughput per GPU
50–75%lower serving cost
samequality on your eval set

Pipeline

  1. Profile latency, throughput and memory
  2. FP8 / AWQ quantisation with quality checks
  3. Continuous batching and prefix caching
  4. Speculative decoding with a small draft model
  5. Autoscaling on real traffic patterns

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

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