Verified65% confidenceFactExact time
For a model with L layers, H attention heads, and per-head dimension d, caching n tokens requires VRAM of 2 × L × H × d × n × (precision bytes).
3
Sources
65%
Confidence
Long-term
Relevance
8/8/2026
First Seen
Sources
Related Claims
UnverifiedFour-dimensional tensors typically appear in multi-head attention mechanisms, where the dimension is evenly divided into several parts and then arranged and processed as a four-dimensional tensor.72% similarVerifiedThe self-attention mechanism allows the model to simultaneously see the information of all other tokens in the input sequence when processing each token, and the computational complexity of the attention matrix is O(n²)71% similarUnverifiedThe arithmetic intensity of the MLP layer is B×T, the arithmetic intensity of the attention layer is S×T/(S+T), and during generation the arithmetic intensity of the attention layer is approximately 1 and is completely independent of batch size.71% similarUnverified标准 Scaled Dot-Product Attention 中 Q 与 K 做矩阵乘法得到 n×n 注意力分数矩阵，计算量为 O(n²·d)71% similarUnverifiedDense attention layers require a complete KV Cache, whereas linear layers rely on hidden states.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/707989API
curl https://kongchang.com/api/v1/knowledge/claims/707989MCP
get_claim(id=707989)