NVIDIA has revealed that they have managed to break AI performance barriers with their Blackwell architecture, which is credited to a round of optimizations and hardware power. NVIDIA Manages To Further Optimize Blackwell For Large-Scale LLMs, Fuels Up The Race For "Token Generation" Speeds Team Green has been making strides in the AI segment for quite some time now, but the firm has recently stepped up through its Blackwell-powered solutions. In a new blog post, NVIDIA revealed that they achieved 1,000 TPS, which is also true with a single DGX B200 node with eight NVIDIA Blackwell GPUs. This was done on Meta's 400-billion-parameter Llama 4 Maverick model, which is one of the firm's largest offerings, and this indicates that NVIDIA's AI ecosystem has made a massive impact on the segment. With this configuration, NVIDIA can now achieve up to 72,000 TPS in a Blackwell server, and, like Jensen said in his Computex keynote, companies will now flaunt their AI progress by showing how far they have come with token output through their hardware, and it seems like NVIDIA is entirely focused on this aspect. As to how the firm managed to break TP/s barriers, it is revealed that they employed extensive software optimizations using TensorRT-LLM and a speculative decoding draft model, bagging in a 4x speed-up in performance. In their post, Team Green has dived into several aspects of how they managed to optimze Blackwell for large-scale LLMs, but one of the more significant roles was played by speculative decoding, which is a technique where a smaller, faster “draft” model predicts several tokens ahead, and the main (larger) model verifies them in parallel. Here's how NVIDIA describes it: Speculative decoding is a popular technique used to accelerate the inference speed of LLMs without compromising the quality of the generated text. It achieves this goal by having a smaller, faster “draft” model predict a sequence of speculative tokens, which are then verified in parallel by the larger “target” LLM. The speed-up comes from generating potentially multiple tokens in one target model iteration at the cost of extra draft model overhead.  - NVIDIA The firm utilized an EAGLE3-based architecture, which is a software-level architecture aimed at accelerating large language model inference rather than a GPU hardware architecture. NVIDIA says that with this achievement, they have shown leadership in the AI segment, and Blackwell is now optimized for LLMs as large as the Llama 4 Maverick. This is undoubtedly a massive achievement and one of the first steps towards making AI interactions more seamless and faster. Deal of the Day