Verified65% confidenceFactTime unknown
The token consumption of conversational buffer memory (Buffer Memory) grows linearly with conversation length, with 100% information retention.
3
Sources
65%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
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Related Claims
UnverifiedCommon solutions for long-conversation memory management include sliding window truncation, summary compression, and vector memory, used to address the problem of messages exceeding the token limit due to a limited context window69% similarUnverified智能体对内存要求较高，64GB 内存可秒出结果，32GB 或 16GB 首次对话会较慢需要预热过程69% similarVerifiedThe longer the context, the more the KV cache grows linearly, making memory bandwidth and capacity the main bottleneck for inference throughput.65% similarUnverifiedWhen large models generate long texts, they need to cache the key-value pairs (KV Cache) of all previous tokens, and the memory footprint grows linearly with context length, making it very easy for this to become a memory bottleneck in high-concurrency scenarios.65% similarUnverifiedThe framework uses a distributed file system to build a KV cache pool with a capacity of 100,000 TB, retaining memory for three days for agent scenarios and seven days for human chat scenarios.65% similar
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