NVIDIA Blackwell Enhances AI Inference with Superior Performance Gains
Felix Pinkston Jan 08, 2026 09:09
NVIDIA Blackwell architecture delivers substantial performance improvements for AI inference, utilizing advanced software optimizations and hardware innovations to enhance efficiency and throughput.
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, particularly focusing on the 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, which is critical for 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, extending the productivity of existing NVIDIA GPUs across a wide array of applications and service providers.
TensorRT-LLM Software Boosts Performance
Recent updates to NVIDIA's inference software stack, particularly the TensorRT-LLM, have yielded remarkable performance improvements. Running on the NVIDIA Blackwell architecture, the TensorRT-LLM software optimizes the reasoning inference performance for models like DeepSeek-R1. This state-of-the-art sparse MoE model benefits from the enhanced capabilities of the NVIDIA GB200 NVL72 platform, which features 72 interconnected NVIDIA Blackwell GPUs.
The TensorRT-LLM software has seen a substantial increase in throughput, with each Blackwell GPU's performance improving by up to 2.8 times over the past three months. Key optimizations include the use of Programmatic Dependent Launch (PDL) to minimize kernel launch latencies and 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 can deliver significant performance boosts while preserving accuracy. This capability allows for 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 technology stack. The Blackwell architecture, coupled 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 performance, visit the NVIDIA blog.
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