Nvidia Blackwell doubles LLM training performance in MLPerf Training v4.1

Summary

NVIDIA’s Blackwell platform has made significant strides in AI training performance, particularly in large language models (LLMs). In the MLPerf Training 4.1 benchmarks, Blackwell demonstrated up to 2.2 times more performance per GPU compared to the previous Hopper generation. This leap in performance is crucial for advancing generative AI applications that rely on LLMs for tasks such as text generation, computer code creation, and protein chain modeling. The Blackwell architecture includes new kernels that make more efficient use of Tensor Cores and features higher per-GPU compute throughput and larger, faster high-bandwidth memory.

The Rise of Large Language Models

Large language models (LLMs) have become the backbone of many AI applications, from chatbots to content generation tools. These models require extensive computational resources and memory to achieve high performance. The challenge lies in optimizing LLM performance without sacrificing accuracy, which is where NVIDIA’s Blackwell platform comes into play.

The Need for Efficient Training

Training LLMs is a resource-intensive process that can take weeks or even months. To speed up this process, distributed training, gradient checkpointing, and transfer learning are employed. These strategies allow models to learn faster and become more effective language models. However, the need for more efficient training methods is still pressing, especially as models grow into the trillion-parameter range.

NVIDIA Blackwell: A Leap Forward

NVIDIA’s Blackwell platform is designed to address the challenges of LLM training. In the MLPerf Training 4.1 benchmarks, Blackwell outperformed the Hopper generation in several key areas:

• LLM Fine-Tuning: Blackwell delivered 2.2 times more performance per GPU in Llama 2 70B fine-tuning.
• LLM Pre-Training: Blackwell showed 2 times more performance in GPT-3 175B pre-training.
• Stable Diffusion Training: Blackwell outperformed Hopper by a factor of 1.7 in Stable Diffusion v2 training.

Key Features of Blackwell

The Blackwell architecture includes several features that contribute to its improved performance:

• New Kernels: Blackwell uses new kernels that make more efficient use of Tensor Cores, which are optimized, purpose-built math operations like matrix-multiplies that are at the heart of many deep learning algorithms.
• Higher Per-GPU Compute Throughput: Blackwell’s higher per-GPU compute throughput allows it to run complex models more efficiently.
• Larger and Faster High-Bandwidth Memory: Blackwell’s use of larger, higher-bandwidth HBM3e memory enables it to run benchmarks like GPT-3 175B on fewer GPUs without compromising per-GPU performance.

The Future of AI Training

The advancements in NVIDIA’s Blackwell platform are a significant step forward in AI training. With the ability to train LLMs more efficiently, researchers and developers can focus on creating more sophisticated AI applications. The next generation of AI accelerators, such as the Blackwell Ultra, is expected to offer even more memory and computing power, further accelerating the development of AI.

The Importance of Continuous Improvement

Continuous improvement in AI training performance is crucial for advancing AI applications. NVIDIA’s commitment to delivering more performance and focusing on inference-time compute is a testament to the company’s dedication to pushing the boundaries of AI capabilities.

Table: Key Performance Improvements of NVIDIA Blackwell

Table: Comparison of Blackwell and Hopper Performance

Conclusion

NVIDIA’s Blackwell platform has set a new standard for AI training performance, particularly in large language models. With its improved architecture and higher per-GPU compute throughput, Blackwell is poised to revolutionize the way AI models are trained. As AI continues to evolve, the need for efficient and powerful training platforms like Blackwell will only grow, making it an essential tool for researchers and developers alike.