The Achilles heel of AI: The supply crisis and the colossal…
Delays in the production of NVIDIA's Blackwell superchips and the brutal electrical requirements of its racks threaten to put a brake on the plans of cloud hyperscalers.
The artificial intelligence industry is at risk of suffering an unforeseen slowdown. Despite the excitement around the new NVIDIA Blackwell B200 superchips, promised as the engine that will power the training of the next frontier-scale LLMs, the reality of the supply chain and the physics of electricity are imposing severe limits.
Manufacturing problems and the enormous demand for electrical power have become the Achilles heel of cloud hyperscalers.
Design and manufacturing issues in 3nm
Early Blackwell models suffered from failures in the advanced packaging process known as CoWoS-L (Chip-on-Wafer-on-Substrate with Local silicon interconnect) at TSMC. This complex network of adjacent chips interconnected at the micron level experienced performance failures and cracks due to differential thermal expansion.
Arquitectura Blackwell B200:
[Dos dies de GPU masivos] ➔ Unidos por interconexión ultra-rápida (10 TB/s)
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Genera calor masivo ➔ Requiere refrigeración líquida (Direct-to-Chip)
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Consumo de rack de 72 GPUs ➔ 120kW (Consumo promedio de 100 hogares)
NVIDIA was forced to redesign the GPU silicon skin to mitigate heating issues, which delayed large-scale deliveries. Companies that were hoping to deploy massive clusters in the first quarter of 2026 have had their plans postponed for several months.
The challenge of powering data centers
In addition to the manufacturing bottleneck, data centers face the limit of the electrical grid. A single rack with the Blackwell NVL72 architecture (which includes 72 interconnected GPUs) consumes approximately 120 kilowatts of power.
This figure is astronomical. To put it in scale, powering a single hall of servers equipped with these chips requires more energy than entire electrical transformation stations in residential areas have available. The need to incorporate direct-to-chip closed-loop liquid cooling is requiring data centers to restructure their pipelines and infrastructure, substantially increasing operating costs and availability of computing time.
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