NVIDIA has unveiled significant advancements in AI inference performance through its Blackwell architecture, according to a recent blog post by Ashraf Eassa on NVIDIA’s official blog. These enhancements are aimed at optimizing the efficiency and throughput of AI models, with a particular focus on Mixture of Experts (MoE) inference.
### Innovations in NVIDIA Blackwell Architecture
The Blackwell architecture integrates extreme co-design across various technological components, including GPUs, CPUs, networking, software, and cooling systems. This synergy enhances token throughput per watt, a critical factor in reducing the cost per million tokens generated by AI platforms.
The architecture’s capacity to boost performance is further amplified by NVIDIA’s continuous software stack enhancements. These improvements extend the productivity of existing NVIDIA GPUs across a wide array of applications and service providers, enabling greater performance and efficiency.
### TensorRT-LLM Software Boosts Performance
Recent updates to NVIDIA’s inference software stack, particularly the TensorRT-LLM, have yielded remarkable performance gains. Running on the NVIDIA Blackwell architecture, TensorRT-LLM optimizes reasoning inference performance for models like DeepSeek-R1, a state-of-the-art sparse MoE model.
This model leverages the enhanced capabilities of the NVIDIA GB200 NVL72 platform, which features 72 interconnected NVIDIA Blackwell GPUs. Over the past three months, the TensorRT-LLM software has improved each Blackwell GPU’s performance by up to 2.8 times.
Key optimizations include the use of Programmatic Dependent Launch (PDL) to minimize kernel launch latencies, alongside various low-level kernel enhancements that more effectively utilize NVIDIA Blackwell Tensor Cores.
### NVFP4 and Multi-Token Prediction
NVIDIA’s proprietary NVFP4 data format plays a pivotal role in enhancing inference accuracy while maintaining performance. The HGX B200 platform, comprising eight Blackwell GPUs, leverages NVFP4 and Multi-Token Prediction (MTP) to achieve outstanding performance in air-cooled deployments.
These innovations ensure high throughput across various interactivity levels and sequence lengths. By activating NVFP4 through the full NVIDIA software stack—including TensorRT-LLM—the HGX B200 platform delivers significant performance boosts while preserving accuracy.
This capability enables higher interactivity levels, enhancing user experiences across a wide range of AI applications.
### Continuous Performance Improvements
NVIDIA remains committed to driving performance gains across its entire technology stack. The Blackwell architecture, combined with ongoing software innovations, positions NVIDIA as a leader in AI inference performance.
These advancements not only enhance the capabilities of AI models but also provide substantial value to NVIDIA’s partners and the broader AI ecosystem.
For more information on NVIDIA’s industry-leading AI inference performance, visit the [NVIDIA blog](https://blogs.nvidia.com).
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