Unverified50% confidenceTradeoffExact time
Aggressively batching prefill can improve throughput but inflates TTFT, while guaranteeing TTFT constrains prefill batch sizes, creating a conflict between the two optimization objectives.
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
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Related Claims
Unverified传统混合部署中Prefill阶段的大批量计算会阻塞Decode阶段的token生成，导致TTFT波动剧烈73% similarUnverifiedThe High configuration with extended chain-of-thought reasoning needs to process a large number of internal reasoning tokens during the prefill stage, so TTFT is typically several to tens of times that of the standard version.69% similarUnverifiedFlash-level models typically trade smaller parameter sizes or more aggressive quantization strategies for lower time-to-first-token (TTFT), pursuing cost-effectiveness in high-frequency scenarios where "good enough" suffices.68% similarVerifiedInference latency is typically decomposed into two core metrics: time to first token (TTFT) and subsequent token generation throughput.65% similarUnverifiedHigh aggregate throughput often comes at the cost of larger batch sizes, and large batches drive up the time-to-first-token (TTFT) latency for individual requests.65% similar
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