The field of artificial intelligence is the current hot-button topic in the tech industry, with designers and engineers applying it everywhere possible, ranging from operating rooms to power plants to industrial AI box PCs for the factory floor. Therefore, companies that manufacture AI-capable computer hardware and processors are some of the most closely watched businesses in the world.
In the U.S., the two companies leading the charge on AI hardware development are NVIDIA and Advanced Micro Devices (AMD). These two businesses have been fierce competitors long before the current AI boom, and this competition shows no signs of stopping as both seek to develop the best AI-capable processors possible.
Company Overview: The History of NVIDIA and AMD
Given the stakes of their competition, understanding the histories of NVIDIA and AMD is key to understanding the current market for AI-enabling computer hardware.
Why NVIDIA Dominates AI Hardware
Founded in 1993 by computer hardware professionals from multiple other computer hardware companies (including AMD), NVIDIA’s start began with graphical processing units, or GPUs, for PC gaming and commercial purposes. This specialization led to NVIDIA’s expertise in parallel processing, where multiple processors in the same chip accomplish several tasks simultaneously.
Parallel processing would later be adapted as the primary computing method for machine learning and modern generative AI, thereby catapulting NVIDIA to its current position as one of the most valuable tech companies in the world. NVIDIA GPUs are now found powering AI applications across multiple industries, including healthcare and manufacturing.
How AMD is Catching Up
Established in 1969, AMD began as a semiconductor manufacturer before branching out into a wide range of computer chips, including GPUs, CPUs, field-programmable gate arrays, and much more.
In recent years, AMD has made serious moves to better compete with NVIDIA and its position as the current market leader for AI computer chips. These moves include acquiring the Finnish startup Silo AI for $655 million to better develop customized large language models.
Comparing NVIDIA and AMD for AI Workloads
When comparing NVIDIA and AMD’s offerings, it can be easy to get drawn into comparing benchmark tests, such as the MLPerf benchmarks for image classification, natural language processing, and medical imaging. In these tests, NVIDIA consistently outperforms its competition in terms of speed and accuracy. Such numbers are only one part of the equation, however.
The Power of CUDA: Why Developers Prefer NVIDIA
Just as critical for effectively using AI is how well the chip’s manufacturer supports its products and their intended roles. A major driver for NVIDIA’s current success is not just its industry-leading hardware but also its extensive software support. NVIDIA’s CUDA (Compute Unified Device Architecture) toolkit makes it far easier to develop AI-powered applications for a variety of roles and is constantly updated to keep pace with the latest hardware. On top of that, NVIDIA also offers over 600 pre-trained models for developers to use, covering subjects like generative tasks, computer vision, and pharmaceutical drug discovery.
By comparison, AMD does not have the same depth of experience in AI development and deployment or the support options that NVIDIA offers. While AMD’s broad product portfolio means it can provide partners with every hardware component necessary for AI operations at a lower cost, those products might not perform to the same specifications or come with the same software options as NVIDIA’s.
AMD and Triton: Is Open-Source AI the Future?
AMD is working to catch up on this front, such as by participating in the Triton project launched by OpenAI and implementing its own toolkit called ROCm. Triton and ROCm are open-source programming tools designed specifically for working with GPUs and AI and are meant to compete directly with NVIDIA’s CUDA toolkit. However, neither is as developed as CUDA, and both are incompatible with NVIDIA’s GPUs, meaning developers may be putting themselves at a disadvantage if they choose to implement them.
Final Verdict: Which AI Hardware Should You Pick?
When implementing AI in your healthcare group or business’s workflow, the question of hardware selection is one of the most important to make. At Cybernet, all of our industrial and medical AI box PCs use NVIDIA GPUs, supported by decades of design expertise in designing and manufacturing computers for harsh work environments. Contact our team today to learn more about our AI computer offerings.