Nvidia GTC 2025 – Spread Betting

Nvidia is spreading its bets wide and early.  

• Another confident keynote from Jensen Huang where technical slip-ups were part of the show and where the best was saved for last.
• Nvidia is leveraging its dominant position in AI training to move quickly into nascent adjacent markets such that when they start to develop, Nvidia will already be the go-to provider.
• This is how Nvidia can keep competition at bay and still earn very high margins on the chips that it develops and sells.
• During the keynote, the main announcements were:
  • First, Blackwell Ultra & Rubin, where Blackwell is now in full production, Blackwell Ultra was announced and more details of the 2-year roadmap were given.
  • Blackwell Ultra is an update to Blackwell which offers 50% greater AI performance than the original and upgrades both memory size and memory speed.
  • Blackwell Ultra will be available in H2 2025 and I do not expect to see a repeat of the problems that we have seen with the ramp-up of Blackwell as this is an evolution rather than something brand new.
  • The brand-new item appears in 2026 with the launch of Rubin which promises another big jump over Blackwell, but smaller than the jump Blackwell made over Hopper.
  • Rubin is coming in H2 2026 and will offer a 3.3x AI performance gain over Blackwell Ultra as well as a doubling of memory bandwidth.
  • Rubin is two dies stuck together but Rubin Ultra coming in H2 2027 is four GPUs stuck together in a single chip taking the improvement over Blackwell to 14x and 4x over Rubin (of which 2x is because there are two more GPUs).
  • These kinds of gains will certainly allow Nvidia to keep its leadership and Nvidia is following its usual strategy of sharing the gains it makes with the customers.
  • Hence, I would expect that Rubin will be double the price of Blackwell meaning that the customer should see a cost reduction relative to the compute output of 50% or so.
  • This is where the classic “the more you buy, the more you save” tagline comes from, and this looks like it will still be the main theme of the company for a few more years.
  • With data centre capex forecasted to increase to $800bn by 2028 (from $500bn in 2025) and to cross $1tn soon after, it looks like there will be plenty of money available to spend on Nvidia GPUs even as prices continue to rise.
  • Second, Nvidia Dynamo which is a software toolkit aimed at optimising “reasoning” models to run inference on Nvidia silicon.
  • This makes complete sense as “reasoning” is the latest trick to improve performance, but it involves massive increases in compute consumption for inference.
  • This is evident in the prices that OpenAI is charging for its Deep Research service and so it makes sense to offer something that can provide an improvement for this kind of inference.
  • Here Nvidia is claiming that Dynamo can increase the number of tokens generated by 30x when running DeepSeek R1.
  • I suspect that Dynamo is taking advantage of some of the techniques that DeepSeek has put into its model to achieve this level of improvement and so the improvement seen with other models won’t be as good as this.
  • However, there are signs everywhere that the industry is trying to reverse-engineer what DeepSeek has done, and so it is quite likely that other models will also see similar levels of gains in time.
  • Third, Robotics: which I think has the potential to be a huge opportunity, but is going to take much longer than anyone thinks.
  • This is what Nvidia refers to as Physical AI and the combination of Omniverse and Cosmos allows for robotic systems to be trained and tested virtually before they are ever built.
  • The first robots are autonomous vehicles which Nvidia thinks are imminent but where I am considerably more cautious.
  • To kick-start this market, new Cosmos models have been released which can be used together with the new blueprints for Omniverse to train robots and autonomous vehicles.
  • The real win in automotive however was the announcement that GM will be using Nvidia for almost all of its AI needs from digital twins of its factories to running its autonomous cars as well as its corporate AI needs.
  • Nvidia also announced the launch and availability to open-source of Isaac GROOT N1 which is a model that Nvidia says can be used to train all sorts of robots.
  • With this model, Nvidia claims that the age of general robotics is here but I am more sceptical.
  • Just as LLMs are not really general in that they can’t deal with situations they have not been trained for, robots have to be trained individually and then retrained if any changes are made.
  • Fixing this problem is one of the big issues for robotics and so I am somewhat sceptical that GROOT N1 is the fix for this sticky problem.
  • However, what we are seeing is Nvidia moving early and aggressively to cover this nascent space so that when everyone else arrives as the segment takes off, it is already the industry standard.
  • Fourth, DGX Boxes: with a DGX Spark (Mac Mini size) and DGX Station (Desktop PC size) devices launch that brings Blackwell out of the datacentre.
  • DGX Spark can deliver 1 PetaFLOP while DGX Station can do 20 Peta FLOPs with the same code that is used in the data centre.
  • This allows developers to fine-tune their AI services at the edge before deploying them to the cloud or wherever they intend to run them.
  • The big winner here is MediaTek which helped with the design of the DGX Spark and got several mentions during the keynote.
  • This combined with the collaboration in automotive represents a huge profile boost for MediaTek outside of Taiwan which will help it compete in Europe and North America particularly.
• The net result is that while 2024 was all about reaching a new pinnacle in performance, 2025 is all about taking that pinnacle and leveraging it as widely as possible across different industries.
• We are witnessing an extension of the CUDA strategy from the silicon development platform to many other software platforms and tools that make it easy to develop AI for all industries on Nvidia hardware.
• This means that competitors need to match both Nvdia’s hardware cadence and its software offering which is where most of the competition currently is falling over.
• Nvidia is not standing still and is hoovering up as many partners as it can and it is the likes of Accenture, Deloitte, ENY and Cisco who will help cement Nvidia AI platforms in enterprise customers.
• Nvidia is showing no sign of slowing down meaning that it is quickly expanding into any area where AI will be relevant with the strategy to become the industry standard before its competitors get out of bed.
• This will help the company keep its market position but with 85%+ market share in datacentre GPUs, it will remain a hostage to end demand.
• This means that there will be a few tough quarters when the inevitable correction comes, but there is still no sign of this as spending growth in the data centre remains rampant.
• This combined with its reasonable valuation is why it is the only direct AI company that I would touch from an investment standpoint, but I continue to prefer the adjacencies of inference at the edge and nuclear power as the way to invest in the AI boom.