NVIDIA has introduced recommendations for the co-design of LLM architectures and hardware to maximize performance on GPUs, specifically on the Blackwell architecture.
What Happened
NVIDIA has published a guide on optimizing large language model architectures for specific hardware. Key recommendations include using layer dimensions that are multiples of 128, 256, or 512, and prioritizing model width over depth to increase arithmetic intensity. For Mixture-of-Experts (MoE) models, it is suggested to scale Expert Parallelism to increase throughput. Additionally, the importance of optimizing for NVFP4 quantization is emphasized.
Context
The traditional approach to AI development often focuses exclusively on the software aspects of model architecture. However, shifting to a hardware-aware design strategy allows for accounting for the specific computational characteristics of modern accelerators, such as NVIDIA Blackwell, which is becoming a critical factor for the efficient deployment of systems at data center scales.
Why It Matters for the Industry
For the industry, the shift toward architectural co-design means the ability to significantly push the Pareto frontier between accuracy, latency, and throughput. This changes the economics of inference, making hardware optimization a key tool for competitiveness during large-scale AI solution deployment.
Why It Matters for Users
It is important for developers and researchers to consider these principles (e.g., layer dimension multiples or the advantage of model width) as early as the training stage. This allows for the creation of more efficient, cheaper-to-operate systems that maximize the capabilities of modern hardware and avoid performance penalties during deployment.
What Is Not Yet Known / Limitations
The focus of discussion participants varies from purely research tasks to the economic issues of inference costs, which may influence the priorities for implementing these practices.
Sources
Author
Look at AI, Editorial Staff