Unverified50% confidenceFactExact time
In long-context scenarios, the VRAM overhead of the KV Cache can be several times that of the model weights themselves.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn long-context scenarios (such as models with a 128K token window), the GPU memory occupied by the KV Cache may exceed that of the model weights themselves.77% similarUnverifiedIn cache-intensive, long-context, multi-turn agent workflows, the incremental cost of switching to a more powerful model is diluted by cache reads and does not necessarily mean costs double.73% similarUnverifiedFor million-token-level contexts, if each layer independently maintains a complete KV cache, the GPU memory overhead often exceeds the model weights themselves.72% similarUnverifiedIn autoregressive inference, the memory footprint of the KV cache grows linearly with sequence length and may equal or exceed the weight volume in long-context scenarios.72% similarUnverifiedAccurately estimating a model's VRAM usage requires considering multiple dimensions including KV Cache, inference framework runtime overhead (typically an additional 0.5–2 GB), and activation values.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1008129API
curl https://kongchang.com/api/v1/knowledge/claims/1008129MCP
get_claim(id=1008129)