Unverified60% confidenceFactExact time
The inference process of large language models is divided into two phases, Prefill and Decode, and the computational complexity of the attention mechanism is O(n²).
2
Sources
60%
Confidence
Long-term
Relevance
8/24/2026
First Seen
Sources
本地AI Agent部署太慢?轻量级优化实战指南
Related Entities
Related Claims
VerifiedLarge language model inference is divided into two phases, prefill and decode: prefill is compute-intensive and sensitive to GPU processing power, while decode is a memory-bandwidth-bound task.77% similarUnverifiedGlobal self-attention has a modeling complexity of O(n²), and subsequent research has developed relative positional encoding schemes such as RoPE and ALiBi to enhance long-text processing capability.76% similarUnverifiedCurrent mainstream large language models generally adopt the Transformer architecture, whose self-attention mechanism has a computational complexity that grows quadratically with sequence length (O(n²)) when processing long contexts.75% similarUnverifiedTONGYI Lingma's underlying layer relies on the code understanding and generation capabilities of the Tongyi Qianwen series of large language models.70% similarUnverified原始word2vec需要在整个词汇表上计算softmax归一化,计算复杂度为O(V),负采样将问题转化为二分类以降低计算量70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/796904API
curl https://kongchang.com/api/v1/knowledge/claims/796904MCP
get_claim(id=796904)