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The saved model assigns four hundred billion parameters and twelve trillion tokens. Dense training operations are six times parameters times tokens, or two point eight eight times ten to the twenty five. Choosing mixture of experts with eighty five billion active parameters uses twenty one point two five percent of the dense operation count, leaving seventy eight point seven five percent outside this formula. At three pixels per percent dense one hundred measures three hundred, active twenty one point two five sixty three point seven five and difference seventy eight point seven five two hundred thirty six point two five. These are assigned arithmetic assumptions, not executed training, measured sparse dispatch or real model quality. The canvas draws eight illustrative racks and six spines regardless of the sixteen thousand three hundred eighty four chip setting. Memory uses total parameters even in the active-parameter compute case. Four hundred billion times two bytes gives point eight decimal terabytes of weights. Ten additional bytes per parameter gives four terabytes of optimizer states; total four point eight. At sixty pixels per terabyte weights span forty eight pixels, optimizer two hundred forty and combined two hundred eighty eight. Adjacent segments show both components using the same decimal units. The aggregate HBM formula instead divides chip count times gigabytes by one thousand twenty four squared and calls the result petabytes, mixing binary-style conversion with the decimal memory estimate. No actual allocation, optimizer state or out-of-memory test is performed. The source never validates whether a training configuration fits real hardware. Initial PUE is one point two two. Changing it to one point four and clicking reset leaves it at one point four: reset invokes the Alibaba preset, which never sets PUE. At one hundred pixels per PUE unit initial one point two two measures one hundred twenty two, retained one point four one hundred forty and difference point one eight eighteen. That reset also changes the initial four hundred gigabit network choice to six hundred, rather than restoring every original field. Resource metrics multiply assigned chip power, twenty five percent host overhead, PUE and estimated duration. Native controls, positive canvas buffers and actual JSON and text exports describe these local settings, not current vendor specifications, real cluster utilization, electricity bills, carbon measurements or a comparative model benchmark. Preserve the original reset scope and check the downloaded configuration.