Unverified50% confidenceFactExact time
Fast-dLLM inference reuses KV states across denoising steps to accelerate generation, but because bidirectional token representations change as masked tokens are progressively revealed, the reused cache may already deviate from the result of full recomputation.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/30/2026
First Seen
Valid until: 1/28/2027
Sources
Related Entities
Related Claims
VerifiedThe KV cache grows linearly with context length, increasing the memory read volume per step in the decoding phase, which is the fundamental reason decoding speed decreases as context grows.75% similarUnverifiedTokenSpeed's MLA decode kernel has about half the latency of TensorRT-LLM under typical speculative decoding workloads.74% similarUnverifiedWhether the KV cache hits significantly affects inference latency and cost, and a stable prompt prefix with a clear structure helps speed things up and reduce costs.74% similarUnverifieddLLM缺失KV Cache机制，每轮去噪都需对全序列重新计算注意力，导致实际速度提升远低于理论值73% 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.73% similar
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