Unverified50% confidenceFactExact time
传统唤醒词检测模型（KWS）参数量通常在100KB到1MB量级，需在毫瓦级功耗下以极低延迟持续运行
1
Sources
50%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
UnverifiedRive's runtime footprint is extremely small, typically requiring only a WASM runtime of a few dozen KB69% similarUnverifiedThe KV Cache of a 70B-parameter model with 128K context support can require hundreds of gigabytes of VRAM at full load, exceeding the 80GB VRAM capacity of a single H100.68% similarUnverifiedKimi K2在FP16精度下仅模型权重就需要数百GB显存，INT4量化可将显存需求压缩至约四分之一66% similarUnverifiedThe VRAM consumption of the KV cache is proportional to context length, number of model layers, and number of attention heads; under an 8K context, a typical 27B model may consume an additional 2–4 GB of VRAM.65% similarUnverifiedFor a typical 7B model with 32 layers and 32 attention heads, storing the KV Cache for each token requires approximately 0.5 MB of VRAM, so generating 1,000 tokens requires an additional ~500 MB of VRAM65% similar
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