Digital Event Horizon
NVIDIA Takes Generative AI to New Heights: Boosting Performance and Efficiency with Cutting-Edge Optimizations
NVIDIA optimizes state-of-the-art community models like Meta's Llama, Google's Gemma, Microsoft's Phi, and its own NVLM-D-72B. The company achieves significant performance improvements, such as a three-and-a-half times increase in minimum latency performance within less than a year. NVIDIA submits a platform with the Blackwell platform, which delivers four times more performance than the previous generation on MLPerf Inference v4.1's Llama 2 70B workload. Hopper performance increases by three-and-a-half times in MLPerf on H100 thanks to regular software advancements. TensorRT-LLM is a purpose-built library designed to accelerate LLMs with state-of-the-art optimizations for efficient inference on NVIDIA GPUs. Parallelization techniques like tensor and pipeline parallelism allow organizations to split the model across multiple GPUs, leveraging NVIDIA NVLink and NVSwitch interconnect technologies. The relentless pursuit of optimization results in significant performance gains from ongoing software tuning and optimization, enhancing ROI for customers.
Artificial intelligence (AI) has revolutionized the way we live, work, and interact with one another. In recent years, generative AI models have emerged as a game-changer in various industries, including healthcare, finance, and entertainment. These models enable unprecedented opportunities for organizations to gain deeper insights from their data reservoirs and build entirely new classes of applications.
However, the application of these powerful models comes with significant challenges. Both on-premises and cloud-based infrastructure face demanding requirements to deliver high throughput and low latency simultaneously, placing substantial demands on data center resources. In order to mitigate these challenges and drive continuous performance improvements, NVIDIA has been optimizing state-of-the-art community models, including Meta's Llama, Google's Gemma, Microsoft's Phi, and its own NVLM-D-72B.
Performance Improvements
NVIDIA's relentless pursuit of optimization has resulted in significant advancements in the field. For instance, improvements to the open-source Llama 70B model have already led to a three-and-a-half times increase in minimum latency performance within less than a year. Moreover, NVIDIA regularly publishes performance updates, allowing customers to harness more from the same GPUs.
The company's commitment to continuous improvement has been evident in its recent submissions to the MLPerf Inference 4.1 benchmark. For the first time, NVIDIA submitted a platform with the Blackwell platform, which delivered four times more performance than the previous generation. This achievement was made possible by advanced quantization techniques and the second-generation Transformer Engine.
The Blackwell B200 delivers up to four times more performance versus previous generations on MLPerf Inference v4.1's Llama 2 70B workload, further demonstrating NVIDIA's relentless pursuit of optimization.
Improvements in Hopper and Acceleration
In addition to the Blackwell platform, NVIDIA has continued to accelerate its Hopper architecture. Over the past year, Hopper performance has increased by three-and-a-half times in MLPerf on H100 thanks to regular software advancements. This represents a tenfold increase in peak performance compared to just one year ago on Hopper.
The ongoing work is incorporated into TensorRT-LLM, a purpose-built library designed to accelerate LLMs with state-of-the-art optimizations for efficient inference on NVIDIA GPUs.
Parallelism Techniques
One of the critical factors that determine the optimal performance of generative AI models is parallelization techniques. These techniques allow organizations to split the model across multiple GPUs, leveraging NVIDIA NVLink and NVSwitch interconnect technologies.
The use of tensor parallelism can deliver over five times more throughput in minimum latency scenarios, while pipeline parallelism brings 50% more performance for maximum throughput use cases. However, the optimal approach depends on application requirements, with different scenarios necessitating different techniques.
NVIDIA's platform provides the ability to effectively combine both tensor and pipeline parallelism, allowing customers to maximize throughput within a given latency budget.
The Virtuous Cycle
The relentless pursuit of optimization by NVIDIA results in significant performance gains from ongoing software tuning and optimization. These improvements translate into additional value for customers who train and deploy on the company's platforms. They are able to create more capable models and applications and deploy their existing models using less infrastructure, enhancing their return on investment (ROI).
As new LLMs and other generative AI models continue to emerge, NVIDIA will remain at the forefront of optimizing these platforms, making them easier to deploy with technologies like NIM microservices and NIM Agent Blueprints.
In conclusion, NVIDIA's commitment to cutting-edge optimizations has enabled significant advancements in the field of generative AI. By harnessing the power of its platform and relentless pursuit of performance improvements, the company is empowering organizations to unlock new opportunities for innovation and growth.
Related Information:
https://blogs.nvidia.com/blog/llm-inference-roi/
https://www.marketscreener.com/quote/stock/NVIDIA-CORPORATION-57355629/news/NVIDIA-What-s-the-ROI-Getting-the-Most-Out-of-LLM-Inference-48034855/
Published: Wed Oct 16 01:00:10 2024 by llama3.2 3B Q4_K_M
