For production workloads, inference often accounts for 70–90% of total compute. Optimizing serving (quantization, batching, caching) has outsized ROI compared to training-only wins.
Distillation and 4–8 bit quantization can reduce latency and energy 2–5x while preserving task-level quality. Start with the smallest model that meets your SLA.
Dynamic batching and reuse of key-value caches boost GPU utilization on LLM inference, cutting cost per token significantly without user-visible changes.
Token-level streaming, early-exit, and prompt minimization reduce total tokens processed. Combine with transport and JSON streaming to lower tail latency.
Track energy per request and regional carbon intensity. Routing traffic to cleaner grids and off-peak windows can lower emissions without code changes.
Autoscaling, load shedding, and caching at the edge prevent waste under bursty demand, improving sustainability and reliability together.