Unverified50% confidenceFactExact time
Traditional LLM inference frameworks pre-allocate contiguous memory blocks for each request when handling KV Cache, leading to memory fragmentation and waste.
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50%
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Long-term
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9/19/2026
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VerifiedThe 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.75% similarUnverifiedDuring inference, each token requires maintaining a KV cache (Key-Value cache), whose GPU memory usage is proportional to sequence length and the number of model layers.72% similarUnverified潜在缓存技术将LLM推理中的KV Cache思想迁移至训练阶段，对轨迹历史帧预先提取潜在表示并缓存72% similarUnverifiedIn llmfit, selecting a model and pressing lowercase P enters planning mode, while uppercase S allows temporarily simulating different memory capacities.72% similarUnverifiedLLM推理的prefill阶段并行处理输入所有token并生成初始KV Cache，是计算密集型操作，是TTFT的直接决定因素71% similar
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