AMD’s 2nm leap is real, but Nvidia’s dominance runs deeper than silicon
In sum – what to know:
AMD jumps to 2 nm first – The Instinct MI450 will be the first GPU built on TSMC’s 2 nm process, with Oracle and OpenAI deployments slated for H2 2026.
HBM4 memory breaks the wall – Each accelerator is expected to feature up to 432 GB of HBM4 delivering 19.6 TB/s bandwidth.
Nvidia’s ecosystem still looms large – CUDA’s software lead and entrenched data-center integrations may blunt AMD’s process advantage.
The AMD Instinct MI450 is proving to be one of the most highly-anticipated AI accelerators so far. Oracle and AMD have announced a partnership that will see an initial deployment of 50,000 MI450 series GPUs starting in Q3 2026, into Oracle Cloud Infrastructure data centers.
This is the second mammoth deal in as many weeks for AMD, following an initial deal between AMD and OpenAI, which will involve the deployment of 6 gigawatts of AMD GPUs, starting with 1GW of AMD Instinct MI450 series GPUs in the second half of 2026.
So what makes the Instinct MI450 series so special? Well, it boils down to the convergence of a few new technologies, but starts with the fact that they’ll be among the first, if not the very first, GPU to be built on TSMC’s 2nm process.
According to TSMC, the 2nm process is expected to deliver a 30% power improvement and a 15% performance gain over the previous 3nm node. Both power and performance have proven to be somewhat inescapable bottlenecks in the deployment of AI compute, and even much more modest gains would have significant impacts on the scalability and total cost of the kinds of large AI clusters that the big players, like Oracle and OpenAI, deploy.
The move to 2nm will have significant implications for AI inference too.
“A smaller process node typically comes with better signal integrity, which can reduce latency,” said Bob Bilbruck, CEO at technology business consulting firm Captjur. “In AI, latency is critical for real-time processing, particularly in applications like autonomous driving or real-time inference in edge computing.”
The MI450 is more than just a node switch
It should be noted that some reports indicate that not all of the components in the Instinct MI450 will be built on the 2nm process — but rather only the main computer core of the accelerator. However, this doesn’t necessarily mean that the accelerator will have too many bottlenecks to actually make good on the performance promises of 2nm. Most of the performance benefits of 2nm come from the computer logic anyway, after all.
Indeed, if there were a bottleneck associated with the Instinct MI450, it would relate more to the memory. As the compute logic of accelerators improves, the “memory wall” is becoming an increasingly challenging issue to overcome. The concept of the memory wall is that the slow speeds and limited bandwidth of the memory in an accelerator simply can’t keep up with the increasingly fast compute speeds of processors.
Thankfully, the Instinct MI450 at least attempts to address this with the fourth generation of high-bandwidth memory, or HBM4. Each Instinct MI450 GPU will have up to 432GB of HBM4 memory, and offer a memory bandwidth of up to 19.6TB/s — at least in its Helios architecture. For reference, the AMD Instinct MI300X offers the previous-generation HBM3 memory, and each GPU has up to 192GB of memory with a memory bandwidth of 5.3TB/s.
Perhaps just as important as the actual GPUs themselves is the Helios rack, which is being sold by AMD as a full AI system for large-scale buyers. The Helios system offers 72 Instinct MI450 GPUs, capable of delivering 1.4exaFLOPS of FP8 performance as a whole.
Beating the competition to the punch
The assumption is that Nvidia will have a serious answer to the Instinct MI450 soon after AMD launches its new accelerators. Nvidia itself has been touting its next-gen Rubin architecture, which is expected to perform up to three times as well as Blackwell Ultra, Nvidia’s current flagship model. However, early rumors indicate that Rubin will be built on the TSMC 3nm process, rather than 2nm — although some rumors point to 2nm for the upgraded Rubin Ultra. Nvidia has yet to confirm details about its next-generation models, however.
Regardless, it’s unlikely Nvidia will take too long to move to 2nm, or even beyond 2nm to a more advanced node.
Still, the AMD Instinct MI450 clearly demonstrates AMD shouldn’t be counted out. Nvidia largely dominates the AI infrastructure space, but AMD is proving it can not only keep up, but in some cases, beat Nvidia at its own game.
“Nvidia has traditionally dominated AI acceleration in the form of GPUs with its CUDA ecosystem. If AMD offers comparable, or even superior, performance with 2nm chips, it might lure some customers away, particularly those interested in exploring alternatives to Nvidia’s ecosystem,” continued Bilbruk.
The ecosystem dominance Nvidia has, of course, may prove too sticky for existing architecture — migrating data centers would be a larger effort than it could be worth, even with performance and efficiency gains. However, as we’ve seen from the recent deals, AMD’s efforts could prove enticing to those building out new AI infrastructure.