Unverified50% confidenceFactExact time
Inference engines such as vLLM and SGLang support caching KV vectors to GPU memory or system memory, allowing subsequent requests that share a prefix to skip redundant computation.
1
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50%
Confidence
Long-term
Relevance
10/6/2026
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Related Claims
UnverifiedThe available memory and VRAM of a local model directly determine the obtainable context window length, because inference requires maintaining a KV Cache in VRAM.69% similarUnverified上下文缓存技术底层依赖KV Cache机制，是vLLM、TensorRT-LLM等主流推理框架的核心优化之一69% 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.69% similarUnverifiedTraditional LLM inference frameworks pre-allocate contiguous memory blocks for each request when handling KV Cache, leading to memory fragmentation and waste.67% similarUnverifiedNormalizing flows can directly reuse the mature inference infrastructure of LLMs, including KV-cache management, tensor parallelism strategies, and inference acceleration techniques such as speculative decoding.67% similar
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