Equation 9 · The Deployment Envelope: Small Models Where the Power Is Not
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the halving the bits per weight halves. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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This reframes what quantisation is for. On a server, low precision is often discussed as a memory-capacity or cost measure. On a device it is primarily a bandwidth and energy intervention : halving the bits per weight halves W in the bound above and halves the bytes crossing the most energy-expensive interface in the system. Meta’s own report on quantised Llama 3.2 models claims “56% in model size”, a “41% average reduction in memory usage” and “2-4x speedup”, with “decode latency improved by 2.5x and prefill latency improved by 4.2x on average” on an Android handset [ 23 ] . That is a vendor measurement on vendor-chosen hardware and should be treated as a claim; but the direction and rough…
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This reframes what quantisation is for. On a server, low precision is often discussed as a memory-capacity or cost measure. On a device it is primarily a bandwidth and energy intervention : halving the bits per weight halves W in the bound above and halves the bytes crossing the most energy-expensive interface in the system. Meta’s own report on quantised Llama 3.2 models claims “56% in model size”, a “41% average reduction in memory usage” and “2-4x speedup”, with “decode latency improved by 2.5x and prefill latency improved by 4.2x on average” on an Android handset [ 23 ] . That is a vendor measurement on vendor-chosen hardware and should be treated as a claim; but the direction and rough magnitude are what the bound predicts.
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