# Batched inference

**Source:** https://promtable.com/glossary/batched-inference

> Batched inference packs multiple prompts into a single GPU forward pass, dramatically improving throughput and unit cost at the cost of per-request latency.

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Batched inference packs multiple prompts into a single GPU forward pass, dramatically improving throughput and unit cost at the cost of per-request latency.

GPU compute on transformer inference is mostly idle between tokens — batching exploits this by serving N requests in parallel through one forward pass. Continuous batching (vLLM's PagedAttention, TensorRT-LLM, sglang) is the 2026 standard: requests join and leave the batch mid-flight as they finish, keeping the GPU saturated. Cost per token at scale drops 5-20× vs single-stream inference. The trade-off is per-request latency variance — your request can stall briefly while the batch swaps in. Production teams in 2026 either accept the variance or run separate low-batch tiers for latency-critical traffic.

## When to use

- High-volume LLM inference at scale.
- Open-weight serving on your own GPUs.

## Common mistakes

- Running latency-critical realtime voice through high-batch inference — variance breaks the UX.
- Sizing batch by GPU memory only — KV cache pressure spikes during long outputs.

## Related terms

- [kv-cache](https://promtable.com/glossary/kv-cache)
- [prompt-caching](https://promtable.com/glossary/prompt-caching)
- [speculative-decoding](https://promtable.com/glossary/speculative-decoding)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/batched-inference
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