Unverified50% confidenceConceptExact time
Long-context models need to keep the key-value caches of a large number of tokens in VRAM; the longer the context, the greater the VRAM usage, hence the higher hardware requirements.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
Open-Source Models Outperform Closed-Source Flagships? GPT-5.6 Faces Open-Source Challenge
bilibili开源项目雷达站10/23/2026
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UnverifiedWhen 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.73% similarUnverifiedThe autoregressive generation process of large language models requires frequent reading of the KV Cache (key-value cache), placing extremely high demands on memory bandwidth and capacity.73% similarUnverifiedThe server side can reuse already-computed parts through the KV cache (key-value cache), significantly reducing repeated computation; the higher the cache hit rate, the more requests the same hardware can handle.72% similarUnverifiedThe upper limit of system memory capacity directly determines the size of model a user can run without sacrificing additional precision.71% similarUnverifiedProduct-key memory requires computing the inner product of the query with all keys; when the number of keys is large, computational complexity rises significantly.71% similar
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