Unverified50% confidenceFactExact time
KV-cache size is proportional to the number of sequence tokens and directly determines the number of concurrent requests a single card can handle; more tokens mean a larger KV-cache per user, and the number of concurrent users on the same GPU shrinks proportionally.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
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Related Claims
VerifiedKV Cache reduces the computational complexity of self-attention from O(N²) to O(N) by caching already-computed Key-Value pairs in GPU memory, but memory usage grows linearly with sequence length.76% similarUnverifiedThe core metric for determining whether a graphics card is sufficient is VRAM size; the larger the model, the better the GPU required, and multiple GPUs connected in parallel may even be needed.72% similarUnverifiedThe output token unit price of large model APIs is typically 2–4 times that of input tokens, because the autoregressive generation mechanism requires one independent forward pass per output token and KV Cache occupies a large amount of GPU memory.72% similarVerifiedThe size of the KV cache is determined by three multiplied dimensions: the size of each record, how many tokens are combined into one record, and whether layers can share caches with each other.72% similarUnverifiedThe KV cache size can be expressed with the formula cache = 2 (K and V) × layers × KV heads × head dim × tokens × 2 bytes, where 2 bytes corresponds to FP16, doubling when using FP32, and halving when using INT8 quantization.71% similar
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