Verified70% confidenceFactExact time
The autoregressive generation process of reasoning models is strictly sequential; KV Cache VRAM usage grows linearly with the length of the thinking token sequence, making memory bandwidth the performance bottleneck.
4
Sources
70%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
UnverifiedLLM inference is autoregressive token-by-token generation with bottlenecks in memory bandwidth and KV Cache management, which is fundamentally different from diffusion model inference.79% similarUnverifiedWithout KV Cache, the model would need to recompute all preceding content every time a new token is generated, with computational complexity growing quadratically with sequence length.77% similarUnverified推测解码技术通过小模型预测草稿、大模型验证的方式加速生成，KV Cache共享机制降低重复请求的计算冗余77% similarUnverifiedWhen handling long contexts, multi-turn dialogues, and complex reasoning chains in inference scenarios, models need to frequently read and write large amounts of intermediate states (such as KV Cache), placing extremely high demands on memory capacity and bandwidth.77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/563250API
curl https://kongchang.com/api/v1/knowledge/claims/563250MCP
get_claim(id=563250)