In the rapidly evolving landscape of artificial intelligence, one truth stands paramount: size matters. As we push the boundaries of what AI can achieve, we face an intriguing paradox: larger models consistently outperform their smaller counterparts, yet they come with polynomially higher costs in both training and inference. This reality has created a significant barrier to entry in the AI field, limiting innovation and democratization of the technology.
The Resource Equation
AI development hinges on two critical resources: data and compute power. While data acquisition presents its own challenges, the computational demands of modern AI systems have become the primary bottleneck in advancing the field. This computational challenge manifests in two distinct phases:
1. Training: The initial process of
developing an AI model
2. Inference: The ongoing use of the
trained model
Understanding the Trade-offs
Scaling laws[1] have provided us with valuable insights into the relationship between model size, compute budget, and model performance. While these laws primarily address training dynamics, the reality of deployment often favors smaller, more extensively trained models due to inference costs.
Reduced model sizes are especially attractive when deployed on-device, offering a great way to reduce inference cost. This solution is also more secure, with the option for deep personalization using local data for in-context learning or even fine-tuning.
One principle remains undisputed: enhanced efficiency benefits both training and inference phases. Greater efficiency within a fixed compute budget will result in superior model performance.
The Exponential Impact
The relationship between model capability and utility isn't linear - it's exponential. Larger models demonstrate dramatically superior performance compared to their slightly smaller siblings. This exponential improvement has transformed compute resources from a mere operational consideration into the primary constraint in AI progress. Want a better model? Simply increase its size and quadruple your compute budget!
Many AI applications can't be deployed if a model has under 99.9% accuracy, so the value of a model goes from zero to absurdly high when the accuracy goes from 99% to 99.9%. This seems easy, right? But you might need to 1000X your compute budget to achieve that 0.9% increase in performance, since the closer to 100%, the more expensive it becomes to improve. For many applications, this is simply not economically viable.
The NVIDIA Monopoly
At the heart of this computational challenge lies a significant market imbalance: NVIDIA's virtual monopoly over AI hardware. Major AI laboratories and tech giants - from OpenAI to Meta AI - rely heavily on NVIDIA's hardware for their operations. However, NVIDIA's dominance isn't solely due to superior hardware engineering.
The Software Ecosystem Advantage
NVIDIA's true competitive moat is the software ecosystem they've carefully cultivated over the past decade. The majority of neural network building blocks are deeply integrated with NVIDIA's ecosystem, creating a dependency that makes migration to alternative hardware platforms challenging, if not impossible.
This software lock-in enables NVIDIA to maintain substantial profit margins on their hardware[2], directly impacting the accessibility and affordability of AI development. The result is a market where progress is constrained not by technological limitations, but by artificial economic barriers.
Breaking the Cycle
NVIDIA is a major contributor to the current state of AI, and the financial gains from their contribution are totally fair. However, as the technology becomes adopted by the industry, it's better for everyone to have healthy competition. The best hardware should win and shouldn't be coupled to the software infrastructure; it should be abstracted by compilers!
This is where Burn & CubeCL enter the picture. We are committed to decoupling the AI ecosystem from hardware manufacturers while providing optimal performance on any device. We aim to reduce the cost of training large models in big data centers as well as enable the deployment of on-device models. All of that with the same codebase, no friction, and ultimate flexibility so that even the most unconventional models can be trained and deployed on any platform.
Looking Ahead
By following the scaling laws, we know that a better compute budget is equal to increased model performance. Software optimization can virtually increase your compute budget at nearly zero cost, making your models better!
However, the future of AI shouldn't be constrained by hardware monopolies or software limitations. By addressing the efficiency challenge head-on, we can create a more open, accessible, and innovative AI landscape. In the following series of posts, we'll describe our approach and how we're working to reshape the AI ecosystem.