Multi-Modal Frontiers & Vector Architecture
Energy-Efficient LLM Sampling
Sampling strategies and decoding choices affect not just output quality but energy and cost per token. Efficient sampling reduces wasted compute through better stopping, caching, and adaptive decoding. The goal is the same answer for less energy, measured on your workload.
Points clés
- Use early stopping and length limits to avoid generating unneeded tokens.
- Cache common prefixes and repeated prompts to cut repeated compute.
- Choose sampling parameters that avoid wasteful high-temperature loops.
- Batch requests to improve throughput and energy per token.
- Quantize and use efficient kernels to reduce per-token energy.
- Measure energy per successful task, not per token alone.
- Profile where energy goes: prefill, decode, or data movement.
- Prefer smaller models or distilled variants when quality allows.
Questions fréquentes
Does sampling affect energy use?
Yes. Longer outputs and high temperatures increase compute and energy per request.
Is batching always more efficient?
Usually for throughput, but it can hurt latency; balance both.
How do I measure energy use?
Use hardware power metrics and divide by useful work completed.
What is the quickest win?
Caching, early stopping, and right-sizing the model.
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