The CUDA Monopoly and NVIDIA’s Pricing Problem: Storm Clouds on the Horizon

NVIDIA’s GPU Leadership Under Threat

NVIDIA has long dominated the GPU market, particularly in AI and high-performance computing (HPC), largely thanks to its CUDA platform. However, the landscape is changing rapidly. Competitors like AMD and cloud giants such as AWS, Google, Meta, and Microsoft are challenging NVIDIA’s dominance. The key driver? Price. With NVIDIA’s GPU prices skyrocketing over the past decade, alternatives are becoming not only attractive but essential for organisations seeking ROI.

Breaking Down NVIDIA’s CUDA Monopoly

CUDA’s dominance stems from its extensive ecosystem: robust tools, mature libraries and developer mind share. For years, it has been the go-to platform for GPU programming, powering workloads in deep learning, rendering and HPC. But today, NVIDIA’s once-unassailable monopoly is showing cracks as viable alternatives emerge.

AMD’s Radeon Open Compute Platform (ROCm)

AMD has steadily chipped away at CUDA’s dominance with its open-source ROCm initiative. ROCm allows developers to leverage AMD GPUs effectively for compute-intensive tasks, challenging the long-held belief that CUDA is irreplaceable.

Tools to Ease the Transition

Several tools have emerged to make it easier for organisations to move away from CUDA and embrace alternative GPU solutions:

• HIPify: Converts CUDA code to run on AMD GPUs with minimal modifications.

• ZLUDA: Enables CUDA applications to execute on AMD and Intel GPUs, albeit with some NVIDIA legal wrangling.

• Scale: Facilitates efficient scaling and tuning of workloads across non-NVIDIA hardware.

  
  

These advancements are eroding NVIDIA’s exclusive hold on GPU programming, enabling competitors to compete more effectively.

Cloud Giants and Custom Solutions

NVIDIA’s pricing has driven major cloud providers to develop their own hardware, bypassing NVIDIA’s expensive offerings:

• AWS Trainium2: Designed for AI training workloads, Trainium2 costs roughly half as much as comparable NVIDIA GPUs while delivering similar performance.

• Google TPU: Google’s Tensor Processing Unit (TPU) offers a tailored alternative for machine learning workloads.

• Microsoft Accelerators: Microsoft is investing in custom accelerators to reduce reliance on third-party GPUs.

  
  

These developments highlight a significant trend: cloud giants are no longer willing to pay NVIDIA’s premium prices, opting instead to innovate in-house.

The NVIDIA Pricing Problem

A Decade of NVIDIA Price Increases

The CUDA Monopoly and NVIDIA’s Pricing Problem: Storm Clouds on the Horizon

In 2016, NVIDIA’s flagship GPU, the P100, cost $5,699. Fast forward to 2024, and their top-tier GPU, the GB200, is expected to cost a staggering $60,000 to $70,000. While the GB200 is far more advanced, this represents a 10x to 11x price increase in just nine years.

AMD’s More Modest Price Increases

By comparison, AMD’s flagship GPU in 2016, the FirePro 9300x2, was priced at $5,999. Today, their latest high-performance card, the MI325x, is expected to cost between $15,000 and $20,000. While AMD’s prices have risen, they haven’t followed the same exponential trajectory as NVIDIA’s.

In the gaming sector in 2013 the Geforce GTX Titan was released at $999 which was expensive for the day but adjusted to today's money that would be $1350 yet the RTX 5090 Is expected to cost over $1900 on release. Again well ahead of inflation. While AMD have maintained pricing of their top tier GPU's, pretty much inline with inflation. I get that there is mind share going on here. NVIDIA are seen as a premium brand and I own a 4090 in my office PC for CAD and video editing. Additionally I own another in my home gaming PC. making this article kinda hypocritical I know. But 4090's are a very good card.

Impact on ROI

NVIDIA’s aggressive pricing is making it harder for data centers to achieve ROI. Many organisations are increasingly questioning whether NVIDIA’s performance gains justify the massive price hikes. In many cases, alternatives like AMD and Intel GPU's or in-house accelerators offer much better value and higher chances of achieving ROI.

The Path Forward

If NVIDIA wants to maintain its leadership, it must address the pricing issue. While the company can argue that its cutting-edge products justify their cost, the market dynamics suggest otherwise. Alternatives are improving rapidly, and customers are becoming less willing to tolerate NVIDIA’s premium pricing.

For AMD, this presents a golden opportunity to capture market share by continuing to offer competitive performance at more reasonable prices. The rise of cloud-native hardware further underscores the need for NVIDIA to rethink its pricing strategy or risk losing its dominance.

The GPU market is at a crossroads. NVIDIA’s once-unassailable position is now under threat from a combination of pricing missteps and increasing competition. The emergence of cost-effective alternatives like AMD’s new MI325x, W7900DS, MI210 and Intel’s Gaudi accelerators, is forcing data centers and enterprises to rethink their reliance on NVIDIA. I genuinely believe that if NVIDIA had kept pricing reasonable, companies like AWS, Google, and others would not have felt such urgency to develop their own products.

While CUDA has been a cornerstone of NVIDIA’s success, the tools and ecosystems that once tied developers to NVIDIA hardware are becoming less restrictive. For NVIDIA, the message is clear: adjust pricing, or face a growing exodus of customers seeking better ROI.

Personal Reflections on ROI

On a personal note, I have found achieving ROI on AMD hardware far easier than on NVIDIA hardware for many years. Even during my time as a crypto miner, I preferred AMD’s RX480 and RX470 GPUs, which cost £200 and £150 in the UK, compared to £850 for an NVIDIA 1080 Ti. Those cards achieved 31 MH/s, 29 MH/s, and 38 MH/s, respectively. While the NVIDIA card was faster, I achieved ROI in less than one-third the time on the AMD cards, making the 1080 Ti look ridiculous in terms of ROI.

Remember, the goal of running a business is to make money and NVIDIA just doesn’t cut the mustard in that metric. 'Fanboyism' will empty your bank account if you let it, a shocking fact at the moment is that between 70% and 80% of AI startups have failed and gone out of business worldwide in the last couple of years. Hardware is usually in the top expenditures in almost all AI companies, whether through renting or purchasing hardware. I can’t help but wonder if those numbers would have been less severe had companies opted for alternative hardware solutions to run their projects.

This trend will only stop when people say 'no' to NVIDIA’s prices and choose to focus on making money instead of losing it. The most powerful super computer in the world is El Capitan, an all AMD system. Why, because there are already viable alternatives and the people who made the decision to build that El Capitan did their research and costed out the project properly before deploying it.

A Cautionary Tale

Sytronix AMD W7800 GPU system

Like I said earlier I have an RTX 4090 in my gaming and office workstation because it gives me the highest frame rates and that matters to me in that scenario. But when it comes to making a profit, I use my head, not my heart and every passing day, other hardware vendors look better and better. If this isn't a cautionary tale for NVIDIA, just look at Intel now. They spent a long time milking their customers and not keeping their eye on the ball. They paid a heavy price for that. If NVIDIA loses their mind share of the market by treating their customers with contempt, it may end up biting them in the proverbial backside.

On that note, the best price-performance card on the market for AI is the W7900DS, which, when correctly tuned, offers almost identical performance and sometimes better performance than the RTX 6000 ADA. Yet, it is half the price, with the former costing $3,499 MSRP and the latter costing $6,800 MSRP. In gaming mind share works, that's why I own a 4090, but in business it doesn't, making money does!

The demographics of NVIDIA's customers have changed dramatically, from gamers to business people over the past couple of years, I suspect they will be far less tolerant of losing money on their investments than gamers will be of buying a premium shiny 4090 GPU to show off their amazing frame rates to friends.

Future Directions

I am in the process of writing a blog at the moment about converting CUDA code to run on GPUs made by other vendors. I will release this in the coming weeks and will cover:

• Scale by Spectral Compute

• HIPify, AMD’s ROCm

• ZLUDA

Stay tuned for a detailed guide to breaking free from CUDA’s constraints and exploring the broader GPU ecosystem. The tools and options available today make it easier than ever to adopt alternatives without sacrificing performance or functionality.