improvements in performance and efficiency. These workloads span from the rapidly growing generative AI market to enterprise inferencing, product design, visualization, and to the intelligent edge. Supermicro has built a portfolio of workload-optimized systems for optimal GPU performance and efficiency across this broad spectrum of workloads.
			
			Use Cases
			 
			• Large Language Models (LLMs)
			• Autonomous Driving Training
			• Recommender Systems
			 
			 
			Opportunities and Challenges
			 
			• Continuous growth of data set size
			• High performance everything: GPUs, memory, storage and network fabric
			• Pool of GPU memory to fit large AI models and interconnect bandwidth for fast training
			 
			
			Key Technologies
			 
			• NVIDIA HGX H100 SXM 8-GPU/4-GPU
			• GPU/GPU interconnect (NVLink and NVSwitch), up to 900GB/s – 7x greater than PCIe 5.0
			• Dedicated high performance, high capacity GPU memory 
			• High throughput networking and storage per GPU enabling NVIDIA GPUDirect RDMA and Storage.
			 
			
			Solution Stack
			 
			• DL Frameworks: TensorFlow, PyTorch
			• Transformers: BERT, GPT, Vision Transformer
			• NVIDIA AI Enterprise Frameworks (NVIDIA Nemo, Metropolis, Riva, Morpheus, Merlin
			• NVIDIA Base Command (infrastructure software libraries, workload orchestration, cluster management)
			• High performance storage (NVMe) for training cache
			• Scale-out storage for raw data (data lake)