Exploring the Future of GPUs: NVIDIA's Blackwell GPU and Next-Gen Chip Innovation

Produced by Daniel Aharonoff & Mogul Media AI -

The Pioneering Weeks of Larger GPUs: The NVIDIA and Cerebras Revolution

In the relentless march of technological innovation, last week was a pivotal moment that left tech enthusiasts and professionals, like myself, brimming with excitement. As a chip designer, I couldn't help but marvel at the leaps and bounds made within the industry. Three headlines stood out in particular: NVIDIA's new Blackwell GPU, Cerebras' 4 trillion transistor chip, and the much-anticipated new analog chip.

NVIDIA's Blackwell GPU: Size Matters

NVIDIA, a leading player in the world of hardware, has been soaring to new heights with its stellar profitability. As a result, my Investment Portfolio is looking considerably healthier. The tech giant recently unveiled its latest innovation, the Blackwell GPU.

This groundbreaking GPU harbors an astounding 208 billion transistors and for the first time, two dies have been integrated in such a way that they operate as a single chip. What does this mean in terms of performance? A four-fold surge in training performance and up to 30 times the inference performance compared to the previous-generation Hopper GPU.

The Trade-Off: Cost vs Performance

But how did NVIDIA manage to bolster the performance by such a significant margin? The answer lies in the size of the area. By doubling the area, they were able to double the performance. However, this decision had a hefty price tag attached to it.

The cost of a chip is directly proportional to the area, contingent on the technology node and volume. NVIDIA had to stick with the n4p process by TSMC, an advanced version of the N4, due to TSMC's ongoing struggles with their 3 nm process.

The implications of this bottleneck extend beyond NVIDIA, disrupting the roadmaps of AMD, Intel, and other chip makers. To offset this, NVIDIA adopted a double-die design packaged using TSMC's Chip on Wafer on Substrate (CoWoS-L) technology. This allowed for better interconnect density and superior speed and bandwidth communication between chips. However, the financial implications of this were not insignificant – the fabrication cost of the GPU more than doubled compared to the previous Hopper GPU.

The Competitive Landscape

In today's fiercely competitive tech landscape, every company is seeking an edge. Hyperscalers like Amazon, Google, and Meta are designing their own custom silicon. AMD and Intel are also vying for a piece of the pie, and startups like Cerebras and Groq are offering solid alternatives.

Despite the pressure, NVIDIA remains a leader in AI hardware, but the company cannot afford to rest on its laurels. As I have previously discussed on Mindburst AI, the relentless pace of innovation in AI and chip design means that companies must constantly adapt and innovate to stay ahead.

The Magic of Lowering Precision

So, where does the second doubling of performance come from? Not from a new process node, but rather from a new numbering format. By lowering the precision of calculations, it's possible to achieve the same tasks with the same level of accuracy. This trick has been the key to NVIDIA's success in enhancing the performance of their GPUs.

In a nutshell, these recent developments in the chip design industry underscore the delicate balance of performance, cost, and competition. As we delve deeper into the realm of AI and advanced hardware, it's clear that those who master this balance will lead the charge towards the future.

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