The field of artificial intelligence is experiencing a revolution with Large Language Models (LLMs) at its core. High-performance inference capabilities are essential for businesses and researchers looking to leverage the power of LLMs. With its Blackwell architecture GPUs, NVIDIA is once again pushing the boundaries of LLM inference, providing users with unprecedented speed and efficiency.
Blackwell Architecture: A Powerful Engine for LLM Inference
NVIDIA’s Blackwell architecture GPUs are designed to accelerate artificial intelligence workloads, excelling particularly in the LLM domain. Their powerful computing capabilities and optimized hardware architecture enable them to handle complex LLM inference tasks at impressive speeds.
NVIDIA recently announced that an NVIDIA DGX B200 node equipped with eight NVIDIA Blackwell GPUs achieved over 1000 tokens per second (TPS) per user when using the Llama 4 Maverick model with 400 billion parameters. This speed was measured by Artificial Analysis, an independent AI benchmark service, further confirming the exceptional performance of the Blackwell architecture.
So, what is TPS? Simply put, TPS is a key metric for measuring LLM inference speed. It represents the number of tokens a model can generate per second. Tokens are the basic units of text and can be words, subwords, or characters. Higher TPS means faster response times and a smoother user experience.
Llama 4 Maverick: A Perfect Combination of Scale and Performance
The Llama 4 Maverick model is the largest and most powerful version in the Llama 4 series. With 400 billion parameters, it can understand and generate complex text and perform a variety of natural language processing tasks.
Such a large model requires significant computing resources for effective inference. The advent of NVIDIA Blackwell architecture GPUs makes real-time inference of Llama 4 Maverick possible, opening new doors for various application scenarios.
NVIDIA also claims that the Blackwell architecture can achieve 72,000 TPS/server in its highest throughput configuration. This indicates that Blackwell can not only provide fast inference speeds for individual users but also support a large number of users simultaneously, meeting the needs of different scales of applications.
Software Optimization: Unleashing the Full Potential of Blackwell
Powerful hardware is only half the battle; software optimization is equally critical. NVIDIA further enhances the LLM inference performance of the Blackwell architecture through a series of software optimization techniques.
TensorRT-LLM: An Engine for Accelerating LLM Inference
TensorRT-LLM is an NVIDIA software library specifically designed to accelerate LLM inference. It leverages various optimization techniques, such as quantization, pruning, and kernel fusion, to reduce the model’s computational load and memory footprint, thereby increasing inference speed.
Quantization reduces the precision of the numbers used in calculations, making them smaller and faster to process. Pruning removes unimportant connections in the neural network, simplifying the calculations. Kernel fusion combines multiple operations into one, reducing the overhead of moving data between them.
These optimizations, when combined, enable TensorRT-LLM to deliver significant performance gains for LLM inference. They are particularly effective on NVIDIA hardware, which is designed to take advantage of these techniques.
Speculative Decoding: A Forward-Looking Acceleration Technology
NVIDIA also employs speculative decoding technology, using EAGLE-3 technology to train a speculative decoding draft model. Speculative decoding is a technique that accelerates inference by predicting the tokens that the model is likely to generate next. By generating possible tokens in advance, the model’s waiting time can be reduced, thereby improving overall inference speed.
The EAGLE-3 model is trained to be a fast, but less accurate, predictor of the next token. The main model then only needs to verify these predictions, which is much faster than generating the tokens from scratch. If the prediction is incorrect, the main model can correct it.
By combining TensorRT-LLM and speculative decoding technologies, NVIDIA has successfully increased the performance of the Blackwell architecture by 4x, making it the fastest LLM inference platform available today.
Latency vs. Throughput: Blackwell’s Flexible Choices
In LLM inference, latency and throughput are two important performance metrics. Latency refers to the time it takes for the model to generate a response, while throughput refers to the number of requests the model can process per second.
Different application scenarios have different requirements for latency and throughput. For example, in real-time conversation applications, low latency is critical to ensure that users receive instantaneous responses. In batch processing applications, high throughput is more important to ensure that a large number of requests can be processed quickly.
NVIDIA Blackwell architecture GPUs can flexibly optimize latency and throughput based on different application needs. It can maximize throughput, balance throughput and latency, or minimize latency for a single user, making it an ideal choice for various LLM application scenarios.
NVIDIA notes in its blog: "Most generative AI application scenarios require a balance between throughput and latency to ensure that many customers can enjoy a ‘good enough’ experience simultaneously. However, for critical applications that must make important decisions quickly, minimizing latency for a single client is paramount. As the TPS/user record shows, Blackwell hardware is the best choice for any task—whether you need to maximize throughput, balance throughput and latency, or minimize latency for a single user."
This means that Blackwell can adapt to a wide range of workloads, from serving many users with a conversational AI to powering critical real-time decision-making processes.
Kernel Optimization: Fine-Tuning for Performance Gains
To further enhance the performance of the Blackwell architecture, NVIDIA has meticulously optimized its kernels. These optimizations include:
Low-Latency GEMM Kernels: GEMM (General Matrix Multiplication) is a core operation in LLM inference. NVIDIA has implemented multiple low-latency GEMM kernels to reduce computation time. GEMM operations are the foundation of most deep learning calculations, and optimized kernels can significantly speed up these calculations.
Kernel Fusion: NVIDIA also applies various kernel fusion techniques, such as FC13 + SwiGLU, FC_QKV + attn_scaling, and AllReduce + RMSnorm. Kernel fusion combines multiple operations into one to reduce memory access and computational overhead. By combining these operations, the amount of data that needs to be moved between memory and the processor is reduced, resulting in faster execution.
FP8 Data Type: Optimization leverages FP8 data types for GEMM, MoE, and Attention operations to reduce model size and take full advantage of the Blackwell Tensor Core technology’s high FP8 throughput. FP8 is a lower-precision floating-point format that can significantly reduce the memory footprint and computational cost of deep learning models without sacrificing accuracy. Using FP8 allows more data to be processed at once, increasing throughput.
These kernel optimizations enable the Blackwell architecture to achieve exceptional performance with minimal latency. The fine-grained optimizations ensure that every part of the hardware is working as efficiently as possible.
Application Scenarios: The Unlimited Possibilities of Blackwell
The exceptional performance of NVIDIA Blackwell architecture GPUs opens new doors for various LLM application scenarios. Here are some possible applications:
Chatbots: Blackwell can provide chatbots with faster response speeds and a smoother conversational experience. Faster response times make chatbots more engaging and user-friendly.
Content Generation: Blackwell can accelerate content generation tasks, such as article writing, code generation, and image generation. The ability to generate high-quality content quickly can improve productivity and innovation.
Machine Translation: Blackwell can improve the accuracy and speed of machine translation. Faster and more accurate translation can facilitate communication and understanding across languages.
Financial Analysis: Blackwell can be used for financial analysis, such as risk management, fraud detection, and portfolio optimization. The speed and accuracy of Blackwell can help financial institutions make better decisions and manage risk more effectively.
Healthcare: Blackwell can be used for healthcare, such as disease diagnosis, drug discovery, and personalized treatment. The ability to analyze large amounts of medical data quickly and accurately can lead to breakthroughs in diagnosis and treatment.
As LLM technology continues to evolve, NVIDIA Blackwell architecture GPUs will play an increasingly important role in more fields, driving innovation and development in artificial intelligence applications. The potential applications are vast and will likely continue to grow as LLMs become more capable.
NVIDIA’s Continuous Innovation
NVIDIA is committed to advancing artificial intelligence technology, and the release of the Blackwell architecture GPUs is another testament to NVIDIA’s ongoing innovation efforts. By continuously improving hardware and software, NVIDIA provides users with more powerful and efficient AI solutions, helping them solve various challenges and create new value.
NVIDIA’s commitment to innovation is not just about hardware improvements, but also about developing the software tools and libraries that make it easier for developers to use its hardware. This holistic approach ensures that the full potential of NVIDIA’s hardware can be realized.
Conclusion
NVIDIA Blackwell architecture GPUs, with their exceptional performance and flexible optimization capabilities, are an ideal choice for LLM inference. They provide unprecedented speed and efficiency for various application scenarios, driving the advancement of artificial intelligence technology. With NVIDIA’s continuous innovation, there is reason to believe that the Blackwell architecture will play an even more important role in the future of artificial intelligence. It represents a significant step forward in the development of AI and will enable new and exciting applications that were previously impossible. The combination of powerful hardware and optimized software makes Blackwell a game-changer for the LLM inference landscape.