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Teardown of Cambricon MLU100-D4 16nm AI Accelerator Card

Joshua
5 hoursago

Cambricon’s MLU100-D4 is a 16nm powerhouse built on the MLUv01 architecture. It offers a dual-mode setup (up to 1.3GHz) and packs 16GB of DDR4 VRAM, delivering a solid 102.4GB/s bandwidth. The card’s biggest draw is its form factor: a half-height, single-slot PCIe Gen3 card that pulls all its power directly from the slot—no bulky external cables required for its 80W–110W workload.

Teardown of Cambricon MLU100-D4 16nm AI Accelerator Card-3cgo

The power delivery system is a "Who's Who" of top-tier silicon. Renesas handles the core with a digital PWM controller and 60A DrMOS for pinpoint accuracy. Texas Instruments (TI) manages the rest of the rails, including memory and LDO regulation. By using Panasonic polymer capacitors, Cambricon ensures that the power delivery remains rock-steady even when the AI is crunching massive datasets.

Teardown of Cambricon MLU100-D4 16nm AI Accelerator Card-3cgo

For reliability, Cambricon went with a dual-MCU setup. Two STM32 chips from STMicroelectronics split the duties—one for heavy-duty system monitoring and one for interface logic. An ADI DS75 sensor keeps a constant eye on thermals. This redundant approach, combined with professional level-shifting, ensures the card stays protected and responsive under industrial conditions.

Teardown of Cambricon MLU100-D4 16nm AI Accelerator Card-3cgo

Physically, the MLU100-D4 is built like a tank but fits like a glove. It’s encased in aluminum alloy with strategic cutouts to save space, keeping the thickness to a slim 18.9mm. Since it’s a passive design, it relies on the server’s wind tunnel to stay cool. Weighing in at just 334g, it’s a masterclass in high-density engineering for modern data centers.

Teardown of Cambricon MLU100-D4 16nm AI Accelerator Card-3cgo

The MLU100-D4 shows that Cambricon knows how to build for the long haul. By pairing its domestic AI silicon with world-class power components from Renesas and TI, it creates a stable, high-density solution for the first generation of AI infrastructure.

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Joshua

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