Amazon attacks a monopoly. With its trainium 2, the Cloud giant is trying to reduce its dependence in Nvidia, whose GPU dominates the high performance and AI calculation market. The ambition exceeds the sole question of semiconductors: this is a life-size test of vertical integration as a strategic lever in the technological industry. A replicable approach or an exception specific to AWS?
Tl; Dr Amazon does it open a way to go against Nvidia?
Amazon tries to get around his dependence in Nvidia with Trainium 2betting on a Extreme vertical integration Rather than on flea performance alone.
Its supercalculator Project Rainier Optimizes each component to maximize system efficiency.
Europein search of technological sovereignty, could it be inspired by this model?
Actors like SIPEARL, ATOS and OVHCLOUD have assets, but the absence of an alternative to Cuda slows down the emergence of a real competitor ecosystem.
During this time, Nvidia retains a strategic advance Difficult to catch up.
Nvidia, technological locking difficult to break
Since 2006, Nvidia has imposed Cuda as standard parallel calculation software. Each IA advance has strengthened this locking: Openai, Anthropic, Google Deepmind and Meta train their models on its GPUs, for lack of a viable alternative. Cuda is not limited to a software interface: it conditions all the tools and libraries used by AI research.
Amazon wants to break this dependence with a radical approach. Rather than betting on the only gross performance, AWS plays the integration card from start to finish. Trainium 2 is not intended to directly surpass the NVIDIA GPU in unitary power, but to be optimized in an infrastructure designed around him. Project RainierAmazon’s supercaluler is based on an architecture designed to maximize the efficiency of the system as a whole. Each component, from wiring to cooling algorithms, is calibrated to use the slightest possible optimization.
The fundamental link between processor and code
A processor does not work alone: he performs instructions defined by the code submitted to him. Each processor architecture is based on a specific instructions game (ISA – Instruction Set Architecture), which defines how the hardware interprets software controls. In the case of AI, accelerators like the NVIDIA or Amazon GPUs are optimized to execute massively parallel calculations in connection with automatic learning algorithms. The dependence of companies in Nvidia therefore does not only come from the power of its chips, but also from the compatibility of its software ecosystem (CUDA) with existing AI models. Changing processor implies rewriting or adaptation of the code, a major technical cost that slows down the adoption of alternatives.
A strategy that can only concern cloud giants
Vertical integration requires massive investments. Amazon bought Annapurna Labs in 2015 to design its own chips, and its partnership with Anthropic shows the way AWS seeks to impose Trainium. Anthropic, who previously used Google Tpus and Nvidia GPUs, agreed to form its next Claude model on Trainium 2. But this transition was not made naturally: Amazon has invested $ 8 billion in the startup.
Can other actors follow this approach? Only Google and Microsoft have the necessary resources. Google has already started this strategy with its TPUS, which equip its data centers and are used to train Deepmind models. Microsoft, for its part, still depends strongly on Nvidia but finances in parallel with the alternatives with AMD and its own Maia chip. On the other hand, for intermediate companies, such integration remains out of reach.
Can Europe and France replicate this model?
Europe is trying to catch up with initiatives such as the project EU CHIPS ACTwhich aims to develop a sovereign semiconductor sector. Siemens,, StmicroelectronicsAnd ASML are key players in this industry, but none has the vertical integration of Amazon. Atosvia its Bull branch, tries to impose its supercomputers but without an alternative to Cuda, the competition remains limited.
In France, Sipearl Develops the RHEA processor for high performance calculation, intended for European supercaluler. This approach, supported by the European Union, could be a first step towards a continental alternative to Nvidia. However, the absence of a software ecosystem comparable to Cuda makes it difficult to adopt these solutions. OVHCloud Could play a key role by integrating these technologies into its cloud infrastructure, but the ecosystem must still be structured.
Towards a technological sovereignty of states?
Amazon’s ambitions are part of a broader trend: the will of several nations to reduce their dependence on foreign semiconductors. Europe and the United States seek to relocate the production of fleas and promote alternative solutions to Nvidia. The example of Amazon could serve as a model for public initiatives aimed at creating a sovereign ecosystem.
However, Nvidia’s advance remains very important. Even if Amazon manages to impose trainium in certain uses, the majority of IA models will continue to rely on Cuda in the months or even years coming.
The battle is therefore not only played on flea performance, but on the ability to reshape a market dominated by a quasi-monopoly. Amazon opens a breach. It remains to be seen if others will be able to rush into it.
