## Introduction In the rapidly evolving landscape of artificial intelligence and machine learning, efficiency and performance have become paramount. As organizations increasingly seek to leverage the power of GPUs, NVIDIA has introduced a groundbreaking approach that redefines how tasks are managed in its ecosystem. This innovation, known as "delegation," marks a significant leap from traditional GPU usage, moving from 8 to an astounding 72 GPUs per domain. This article explores the implications of this shift, detailing how the delegation model operates, its impact on AI performance, and the challenges it presents for local open-weights models. ## Understanding the Delegation Model ### What is Delegation in GPU Management? Delegation represents a new operational paradigm within NVIDIA's Blackwell architecture. Unlike conventional methods where GPUs are assigned specific tasks in a more direct manner, delegation allows users to assign broader tasks that can run for extended periods. This model enables a more efficient use of GPU resources, optimizing the computational power available to organizations. ### The Transition from 8 to 72 GPUs The numbers speak volumes: transitioning from 8 to 72 GPUs per domain is not merely an enhancement of inference capabilities but a transformational shift in how computational resources are leveraged. This change is driven by the increasing complexity of AI models and the need for robust, scalable solutions that can handle larger datasets and more intricate computations. ## The Role of Quality and Scale in Delegation ### Prioritizing Quality over Open-Weights Models One of the most notable aspects of the delegation model is its emphasis on quality. As organizations assign tasks that can run autonomously over extended periods, the results are often more refined and optimized compared to traditional methods. However, this introduces a significant caveat: local open-weights models may find it challenging to access the same level of quality and performance. The delegation system creates a one-way dependency, where the efficiency gains are heavily reliant on the NVIDIA infrastructure and its proprietary capabilities. ### The Impact on AI Performance With the delegation model, AI performance can reach new heights. The ability to manage a greater number of GPUs allows for parallel processing of tasks, which can drastically reduce computation time. This is particularly beneficial for industries that rely heavily on real-time data processing, such as finance, healthcare, and autonomous driving. The speed and scalability provided by this new architecture enable rapid iterations and improvements in AI algorithms, fostering innovation and competitive advantage. ## Challenges and Considerations ### Navigating the One-Way Dependency While the delegation model presents numerous advantages, it also introduces challenges, particularly regarding the dependency on NVIDIA's cloud computing resources. Organizations must weigh the benefits of enhanced performance against the potential risks of losing flexibility and control over their computational processes. Relying solely on NVIDIA's architecture may limit other avenues for innovation and experimentation with local models. ### The Future of Open-Weights Models As the industry shifts towards paradigms like delegation, the future of open-weights models remains uncertain. While these models have historically played a crucial role in democratizing access to AI development, their inability to compete with the efficiency of delegated tasks may create a divide. Organizations will need to assess their strategies carefully, considering how best to integrate open-weights models with the new delegation framework while ensuring they do not fall behind in performance and quality. ## Conclusion NVIDIA's introduction of the delegation model through its Blackwell architecture marks a turning point in GPU management and AI performance. By enabling organizations to harness the power of up to 72 GPUs per domain, this innovative approach offers substantial benefits in terms of quality and efficiency. However, the shift also presents challenges that require careful navigation, particularly concerning the reliance on NVIDIA's ecosystem and the future of open-weights models. As the AI landscape continues to evolve, organizations must adapt and strategize to stay at the forefront of technological advancements, ensuring they maximize the potential of these powerful new tools while maintaining flexibility and innovation. Source: https://blog.octo.com/le-seuil-de-delegation