Blackwell, GPU delegation, NVIDIA, deep learning, open-weights models, high-performance computing, AI inference, delegation mode, GPU scaling, data processing ## Introduction In the era of artificial intelligence and machine learning, the race for computational power is fiercer than ever. Companies are on a relentless quest to enhance their processing capabilities, and NVIDIA is at the forefront of this technological revolution. With the introduction of Blackwell, NVIDIA is redefining the landscape of GPU utilization, transitioning from 8 to a staggering 72 GPUs per domain. This innovation is not merely an incremental improvement in inference but rather a groundbreaking shift towards a new operational mode: delegation. In this article, we will explore the implications of this shift, the mechanics behind delegation, and how it positions NVIDIA as a trailblazer in the field of high-performance computing. ## Understanding the Shift from Inference to Delegation ### The Concept of Delegation At its core, delegation in the context of NVIDIA's Blackwell technology represents a paradigm shift in how tasks are processed. Traditionally, GPUs have been utilized to perform inference tasks—processing data to generate predictions or insights. However, with the delegation mode, users assign a task that can run for hours, leveraging the power of multiple GPUs to handle more complex computations. This new approach allows for a level of scalability and efficiency that was previously unattainable. By distributing workloads across a larger number of GPUs, Blackwell not only enhances performance but also optimizes resource utilization. This shift signifies a departure from conventional methods, where increased performance often meant simply enhancing individual GPU capabilities. ### Implications for Open-Weights Models One significant aspect of this new mode of operation is its impact on open-weights models. In the world of machine learning, open-weights models are those that allow developers to access and manipulate the underlying data structures. However, with the introduction of Blackwell's delegation, the dependency on these open-weights models becomes a question of practicality. As these models will not have direct access to the resources allocated under the delegated mode, developers may need to rethink their strategies. The quality of outputs generated through delegation is directly correlated with the scale of GPU deployment, emphasizing the need for a shift in focus from traditional open-weights models to more integrated approaches that harness the full power of NVIDIA's architecture. ## The Quality of Scale: Why It Matters ### Enhanced Performance Metrics The quality of results produced by computational tasks is paramount in the field of AI and deep learning. As we transition to a delegation model, the quality of scaled outputs becomes a critical metric. The ability to leverage 72 GPUs instead of 8 means not only faster processing times but also the potential for more nuanced and accurate results. This is particularly crucial in applications such as natural language processing and image recognition, where the intricacies of data require substantial computational resources to analyze effectively. By scaling performance, NVIDIA's Blackwell technology promises to deliver superior quality in outputs, enabling organizations to make better-informed decisions based on more accurate data analysis. As industries increasingly rely on AI-driven insights, the emphasis on quality at scale will be a defining factor in competitive advantage. ### A Unidirectional Dependency While the delegation model offers numerous advantages, it also introduces a significant dependency issue. The relationship between task delegation and GPU resources is fundamentally unidirectional. In other words, while the GPUs can efficiently process delegated tasks, the models that rely on open weights cannot reciprocate this computational capacity. This dynamic requires careful consideration by developers and organizations alike. As the landscape shifts, there will be a need for tools and frameworks that facilitate this new mode of operation, ensuring developers can optimize their workflows effectively while navigating the limitations imposed by the lack of direct access to open-weights models. ## Conclusion: The Future of AI with NVIDIA's Blackwell The introduction of Blackwell and its delegation technology marks a pivotal moment in the evolution of AI and high-performance computing. By enabling organizations to utilize 72 GPUs per domain, NVIDIA is setting a new standard for performance and scalability. This shift from inference to delegation not only enhances processing capabilities but also redefines the relationship between models and computational resources. As industries continue to embrace AI solutions, the implications of this technology will reverberate across sectors. From improved quality outputs to the rethinking of open-weights models, NVIDIA's Blackwell is a testament to the power of innovation in driving progress. The future of AI is undoubtedly bright, and with advancements like these, we are just beginning to scratch the surface of what is possible in the realm of computational intelligence. Source: https://blog.octo.com/le-seuil-de-delegation