NVIDIA’s Blackwell architecture is delivering significant performance improvements for AI inference, particularly in handling the demands of sparse mixture-of-experts (MoE) models like DeepSeek-R1. By optimizing the entire technology stack, including GPUs, CPUs, networking, and software, NVIDIA enhances token throughput per watt, reducing costs and extending the productivity of existing infrastructure. Recent updates to the NVIDIA inference software stack, such as TensorRT-LLM, have increased throughput by up to 2.8x, leveraging innovations like NVFP4 data format and multi-token prediction (MTP). These advancements enable NVIDIA’s platforms, like the GB200 NVL72 and HGX B200, to deliver industry-leading performance, efficiently supporting large AI models and enhancing user experiences. This matters because it allows AI platforms to serve more users with improved efficiency and reduced costs, driving broader adoption and innovation in AI applications.
The rapid advancement of AI models has led to an increased reliance on them for a wide array of tasks, necessitating the generation of more tokens during interactions. This surge in demand requires AI platforms to optimize token throughput per watt to minimize costs. NVIDIA’s approach involves a comprehensive co-design strategy that integrates GPUs, CPUs, networking, and more to enhance token throughput efficiency. This strategy not only reduces the cost per million tokens but also extends the productivity of existing NVIDIA GPU infrastructures across various sectors, including cloud service providers and enterprises. By focusing on maximizing performance from existing platforms, NVIDIA ensures that their technology remains relevant and valuable over time.
Recent updates to NVIDIA’s inference software stack, particularly on the Blackwell architecture, have resulted in significant performance improvements. The introduction of multi-token prediction (MTP) on the HGX B200 platform has quadrupled token throughput, showcasing the potential for substantial efficiency gains. The GB200 NVL72 platform, with its extensive interconnectivity and bandwidth, is optimized for sparse mixture-of-experts (MoE) architectures, which are essential for handling complex AI models like DeepSeek-R1. These advancements highlight NVIDIA’s commitment to pushing the boundaries of AI performance, enabling more efficient and cost-effective AI operations.
One of the key innovations driving these performance leaps is the NVFP4 data format, which enhances accuracy while maintaining efficiency. Coupled with the TensorRT-LLM software, these optimizations have dramatically increased the throughput of Blackwell GPUs. The use of Programmatic Dependent Launch (PDL) and low-level kernel optimizations further streamline operations, reducing latency and enhancing throughput across various interactivity levels. These improvements are crucial for developers and enterprises seeking to maximize the capabilities of their AI models, ensuring that they can handle more complex tasks and deliver better user experiences.
NVIDIA’s continuous optimization efforts underscore the importance of staying at the forefront of AI technology. By delivering higher performance across their technology stack, NVIDIA not only enhances the capabilities of AI models but also provides significant value to customers and partners. The Blackwell architecture, combined with the latest software innovations, positions NVIDIA as a leader in AI inference performance. These developments matter because they enable AI to be more accessible and efficient, ultimately driving innovation and productivity across industries. As AI continues to evolve, such advancements will be critical in meeting the growing demands of AI-driven applications and services.
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