Over the last few weeks I’ve taken the time to try to formalise some of the knowledge that I’ve had in my head just ‘swimming around’ and decided to jump into some of NVIDIA’s online training around AI in the Datacenter. It explores a range of the considerations for Storage, Networking, Compute and Management – alongside scale when deploying AI platforms into the datacenter environment.
You can see the course details HERE
The course was broken down into a number of sections for easy consumption
- Introduction to AI and why AI will be key to the Datacenter of the futureOverview of AI and an exploration of use cases to give context and overview of GPU development and associated software.
- Scaling AI platforms, considerations for storage and network deployment as well as reference architectures
- Infrastructure and Cluster Provisioning and Management (aka the software and platforms that make this all deliver value)
- Physical Stuff – Power and Cooling considerations
First, be under no illusion – this is learning AI the NVIDIA way, and in a way that’s no bad thing as their technology is by far the dominant one in the industry today – but having said that a large amount of the learning and understanding is fully transferrable away from the world of NVIDIA.
What really stood out about this course though was that it really gave you an understanding of what is different with GPU computing vs CPU computing, where those two technologies intersect and where the CPU is key vs GPU. You come away with a great understanding of how and why CUDA is so key to NVIDIA’s success – whilst at the same time realising that the power of CUDE is a point of failure for NVIDIA.
Open Source tends to win in the long run
At the moment NVIDIA is pulling form the Apple playbook of ecosystems – they are working hard to ensure that they can own and control as much of the top to bottom stack experience as they can. That will continue to work well in the hardware space, but it is the software space that currently they have such influence and penetration from CUDA as a crucial tool for so many developers that one can’t help but think they are exposed on.
- CUDA Driver and Runtime APIs: – While NVIDIA provides extensive documentation and SDKs for CUDA development, the underlying implementations of the CUDA driver and runtime libraries are proprietary. This closed nature means that NVIDIA controls the optimisation, performance improvements, and compatibility with hardware, which could limit the transparency and adaptability for end users and developers.
- CUDA Compiler – The CUDA compiler, nvcc, which translates CUDA code into binary executables and libraries, is also proprietary. This restricts the ability of the community to extend or optimise the compilation process for specific use cases or environments not officially supported by NVIDIA.
- Performance Profiling and Debugging Tools – Tools like NVIDIA Visual Profiler and Nsight Systems are critical for optimising CUDA applications but are closed source. Their proprietary nature can limit customisation and extension by the developer community.
Why this poses a risk ?
- Vendor Lock-in: The closed source nature of any key components in software (or hardware) can lead to vendor lock-in, where customers are dependent on NVIDIA for updates, support, and compatibility. This can be a deterrent for certain users or organisations that prefer open standards (sometimes even as corporate policy) and value the flexibility to switch between different vendors without significant rework. Crucially this lock-in tends to work well in the early stages of development of a technology or community, as it allows a the vendor to rapidly iterate and control all elements of a stack – however as technology and markets mature, the balance of the value seen from those benefits vs the fear of ‘lock in’ and cost to change starts to shift in favour of Open Source.
- Community Engagement and Innovation: At the moment NVIDIA is the very much the golden child, led by a charismatic leader – however at it’s core I would argue that NVIDIA has passionate loyal fans, as opposed to a contributing community. An entirely or mostly closed source ecosystem can limit community engagement and external innovation. As time goes by I can’t help but think that NVIDIA is going to have to address this issue to protect itself as it moves forward.
- Competition: Competitors might develop more open or flexible platforms. For instance, AMD’s ROCm (Radeon Open Compute) platform is a direct competitor to CUDA and is positioned as a more open alternative. If these platforms become more attractive to developers and researchers, NVIDIA could lose market share in domains critical to its growth, such as machine learning and scientific computing. The other challenge is that this could happen very quickly and be highly disruptive to NVIDIA’s direction.
- Security and Transparency: Closed source components are often criticized for a lack of transparency, making it difficult for independent verification of security claims or for third parties to conduct their own security assessments. This could pose a risk in environments where security and auditability are paramount.
So ….. while the closed source elements of CUDA allow NVIDIA to maintain control over the ecosystem and ensure a high level of performance and reliability, they also pose strategic risks by potentially limiting flexibility for users, inhibiting external innovation, and facing challenges from competitors with more open platforms. Balancing control with community engagement and openness is a nuanced strategic challenge for NVIDIA as it seeks to maintain and expand its dominance in high-performance and parallel computing markets.
In a way that’s a strange takeaway to bring from a course about NVIDIA’s datacenter strategy, but that’s what really stood out to me.