With the release of UDNA (Unified Data and Neural Architecture), AMD has declared a dramatic change in their approach to GPU architecture. AMD’s current RDNA (gaming) and CDNA (data centers) architectures are to be combined into a single, cohesive platform with this new design.

Users contend, however, that AMD has only partially supported CDNA and is more likely to do so in the future. Per-generation optimization is necessary for RDNA. AMD needs to work much harder to support RDNA users as a result.

Since RDNA has a smaller user base, very few developers are prepared to invest more time and energy into creating software, particularly if they are unsure of their future market share.

This indicates that AMD will be able to expedite development across all GPU portfolios using the new UDNA technique. It is expected to compete with NVIDIA’s Ada Lovelace design, which is found in GPUs for consumers, workstations, and data centers.

AMD is attempting to emulate NVIDIA’s success with their GPU portfolio using UDNA. Since the latter had a single architecture for all of its products, anyone with a PC could access their developer ecosystem for business applications and other non-gaming uses.

However, AMD did not follow through on their promise to do so for consumer cards, and when they did, it was a year later.

“This is something they should have done a decade ago but didn’t. And now they’re realising that they will never grow their market share if they don’t follow the leader,” said a Reddit user, suggesting why UDNA is a perfect move from AMD.

Why is UDNA a Big Thing for AMD?

UDNA is designed to provide better scalability for consumer products and data centre solutions. This approach aims to attract more developers by offering a consistent architecture across different GPU types, potentially growing AMD’s market share.

Having single chiplets also simplifies the software stack for AI. Right now, RDNA doesn’t support all the optimisations that CDNA supports for ML workloads, for instance.

“Using the same architecture will make gaming GPUs more ML capable and simplify these optimisation efforts,” a Reddit user added, suggesting how RDNA can simplify software development for AI using AMD hardware.

Anurag Bansal, managing director at 13D research & strategy, mentioned that UDNA will enable AMD to simplify development and improve software compatibility across consumer and data centre GPUs. AMD is also prioritising forward and backward compatibility to avoid losing optimisations in future generations of chips.

Interestingly, AMD had a similar approach called GCN, a “one-size-fits-all” solution, aiming to address both graphics (gaming) and compute (GPGPU) workloads efficiently. It was launched in 2012 and discontinued in 2021. Users are speculating that AMD is about to make the same mistake again.

Well, it is not the same. UDNA is a bit different since it’s a chiplet architecture. They can make IO and compute chiplets that have various capabilities versus scaling a similar chip like GCN. Plus infinity fabric is a great set of technologies to build a unified architecture that didn’t exist for GCN.

Can the Real AMD Please Stand Up?

AIM had said that 2024 will be the year for AMD, and it seems like the market is finally accepting this. AMD’s data centre revenue rose 115% to $2.8 billion in the June quarter compared to the same period a year ago, while NVIDIA’s data centre revenue rose 154% to $26.3 billion in the July quarter.

That is a huge concern for tech companies as they believe NVIDIA has a monopoly over the AI market which is correct and scary. This is why it is necessary to have an NVIDIA competitor and AMD is positioned well to be that.

Meanwhile, Oracle’s senior vice president Karan Batta said Oracle’s use of AMD hardware could also help it guard against potential NVIDIA supply shortages which happened last year.

The software approach is definitely going to take this further. When we consider how it measures up to CUDA, there are still a few problems. One of them is software support. Even when the GCN was in the market, it couldn’t beat NVIDIA as it lacked optimised drivers.

“It’s not just a GCN thing anyway; RDNA1 had the same thing, and so did RDNA3. I think truth be told, AMD rushes their launches and doesn’t make good drivers till they have enough time to optimise,” a Reddit user mentioned.

In the past, AMD was not the preferred option for AI and ML tasks as there were no good libraries for using AMD in ML. But things are about to change. The Finnish LUMI-G supercomputer (5th in computing power in the world, IIRC) is built with AMD hardware.

AMD recently acquired Finnish AI company Silo AI after the acquisition of Mipsology and Nod.ai.

This way, AMD is going to properly seed fund the use of AMD in ML and improve the driver’s situation. NVIDIA is a market leader with its unmatched ecosystem; meanwhile, AMD has a few advantages working in its favour. Unlike NVIDIA, AMD has ROCm, which is an open-source platform, and it has a track record of providing hardware at cheaper prices.

So, if AMD can provide GPUs with large VRAMs at affordable prices, developers will adopt and start building AI projects over it. ROCm being an open-source platform, solutions can be shared anywhere and it might boost the adoption of AMD in AI.