Unverified50% confidenceDecision RuleExact time
The VRAM requirement estimation formula is: VRAM requirement ≈ weight size + KV cache + inference intermediate activation overhead; the weight part is parameter count × bytes per parameter, and the KV cache part is 2 × number of layers × hidden dimension × context length × bytes per element.
1
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50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
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Related Claims
UnverifiedFor multi-turn conversations or long-document question answering, if the KV of the entire context is recomputed on every request, the compute overhead grows quadratically with context length.69% similarUnverifiedKV Cache往往占据推理总显存的30%至70%，是制约大模型推理吞吐量和上下文长度的主要瓶颈66% similarVerifiedThe KV Cache (key-value cache) mechanism accelerates inference by caching the Key and Value vectors generated by the self-attention layer to avoid redundant computation, but the longer the context, the more vectors must reside in VRAM, causing memory pressure to grow in a near-linear fashion.65% similarUnverifiedWhen evaluating an inference engine, throughput and latency must be distinguished: throughput usually refers to the total number of tokens generated by the whole system per unit of time, while latency focuses on the response speed of a single request, divided into first-token latency and per-token generation interval; a larger batch can improve throughput but may raise the latency of a single request.65% similarUnverifiedKV cache size grows linearly with context length, and attention computation grows quadratically with context length.65% similar
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