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with the second term the amortised metadata tax. The OCP MX alliance’s MXFP4 uses w = 4 , k = 32 , and an 8-bit shared scale, for = 4.25 bits per element — a roughly 6% tax [ 4 ] . NVIDIA’s NVFP4 instead uses a smaller block, k = 16 , with the same 8-bit block scale plus a near-negligible per-tensor term, for 4.5 bits per element — a roughly 12.5% tax [ 5 ] . The smaller block buys better local adaptation to each block’s dynamic range, which is the stated reason NVIDIA gives for its accuracy results at 4-bit precision [ 5 ] ; the price is paid in the overhead fraction, s/(kw) , which is exactly double NVFP4’s block-16 tax at block-32. Analysis. That…
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Equation 13 · Semiconductors
AI Accelerator Architecture in 2035: Scenarios, Signals, and Falsifiable Predictions
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
with the second term the amortised metadata tax. The OCP MX alliance’s MXFP4 uses w = 4 , k = 32 , and an 8-bit shared scale, for = 4.25 bits per element — a roughly 6% tax [ 4 ] . NVIDIA’s NVFP4 instead uses a smaller block, k = 16 , with the same 8-bit block scale plus a near-negligible per-tensor term, for 4.5 bits per element — a roughly 12.5% tax [ 5 ] . The smaller block buys better local adaptation to each block’s dynamic range, which is the stated reason NVIDIA gives for its accuracy results at 4-bit precision [ 5 ] ; the price is paid in the overhead fraction, s/(kw) , which is exactly double NVFP4’s block-16 tax at block-32. Analysis. That…
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Equation guide → · Article →Equation 16 · Semiconductors
AI Accelerator Architecture in 2035: Scenarios, Signals, and Falsifiable Predictions
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
with the second term the amortised metadata tax. The OCP MX alliance’s MXFP4 uses w = 4 , k = 32 , and an 8-bit shared scale, for = 4.25 bits per element — a roughly 6% tax [ 4 ] . NVIDIA’s NVFP4 instead uses a smaller block, k = 16 , with the same 8-bit block scale plus a near-negligible per-tensor term, for 4.5 bits per element — a roughly 12.5% tax [ 5 ] . The smaller block buys better local adaptation to each block’s dynamic range, which is the stated reason NVIDIA gives for its accuracy results at 4-bit precision [ 5 ] ; the price is paid in the overhead fraction, s/(kw) , which is exactly double NVFP4’s block-16 tax at block-32. Analysis. That…