AI Chip Deficit – Alternatives to Nvidia GPUs

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In January 2024, leading private equity firm Blackstone announced it was building a $25 billion AI data empire.

A few months later, OpenAI and Microsoft followed suit with a proposition to build Stargate, a $100 billion AI supercomputer that will launch the company to the forefront of the AI revolution.

Of course, this is not a surprise. With the rapid acceleration the AI sector has witnessed over the past few years, industry giants all over the world are in a frantic haste to get front row seats.

Experts already predict the global AI market will hit a massive $827 billion in volume by 2030, with an annual growth rate of 29%.

The only problem? GPUs.

Von Neumann’s architecture, the design model that most general computers operate on – composed of the CPU, memory, I/O devices and system bus – is inherently limited even though it offers simplicity and cross-system compatibility.

The single ‘system bus’ of this architecture restricts the speed at which data can be transferred between memory and the CPU – thus, making CPUs less than optimal for AI and machine learning purposes.

This is where the GPUs (graphics processing units) come in.

By incorporating parallelism as a processing technique, GPUs offer improved performance and independent instruction execution through their multi-cores.

However, with the dawn of AI technology, the demand for GPUs has skyrocketed, straining supply chains and posing a severe bottleneck to the efforts of many researchers and startups.

This is especially true since the world’s supply of GPUs comes from just one major producer – Nvidia.

While hyper-scalers like AWS, Google Cloud Platform and others may be able to easily access A100s and H100s from Nvidia, what are other viable alternatives that can help firms, researchers and startups latch onto the AI train instead of being stuck indefinitely on the Nvidia waitlist?

Field programmable gate arrays

FPGAs (field programmable gate arrays) are reprogrammable, integrated circuits that can be configured to serve specific tasks and application needs.

They offer flexibility, can be adapted to meet varying requirements and are cost-effective.

Since FPGAs are efficient at parallel processing, they are well-suited to AI and machine learning uses and possess distinctively low latency in real-life applications.

An interesting implementation of FPGAs can be seen in the Tesla D1 Dojo chip, which the company released in 2021 to…