What does this equation mean?

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 $w_{\mathrm{eff}}$ = 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 $w_{\mathrm{eff}}$ $\approx$ 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 relationship is the structural reason a further step to 2-bit or ternary elements is not simply “the same trick again”: at fixed block size and scale width, halving element width from 4 bits to 2 bits doubles the relative metadata tax, from 12.5% to 25% at k = 16 . Holding the tax constant requires doubling block size to 32, which is the same move that cost MXFP4 its finer dynamic-range adaptation relative to NVFP4 in the first place. There is no free direction in this trade; every further step down in element width either accepts a growing metadata tax or accepts a coarser shared scale, and which one the industry chooses is not yet settled by any disclosed roadmap.