Unverified50% confidenceFactExact time
In on-device LLM inference, model weights, KV cache, and runtime must all fit simultaneously within the device's fixed memory.
1
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50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
UnverifiedWhen deploying large models locally, VRAM usage consists of two parts: model weights and runtime memory (primarily the KV cache)77% similarVerifiedWhen running local LLMs, VRAM capacity is often more critical than computational speed, as VRAM capacity directly determines which models can be run.73% similarUnverifiedThe key factors determining what models can be run are memory capacity and memory speed: memory capacity determines model size, and memory speed determines the number of tokens per second.72% similarUnverifiedWhen a model runs, its weights need to be loaded into RAM (system memory) or VRAM (video memory), and having large storage capacity does not mean the model can actually run.72% similar
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