Home > AI Solutions > Gen AI > White Papers > Maximizing AI Performance: A Deep Dive into Scalable Inferencing on Dell with NVIDIA > Conclusion
In the preceding sections of this technical white paper, we delved into the impressive AI capabilities of the XE9680 and R760xa servers under various configurations and scenarios. By deploying Llama 3 models, we conducted tests to measure performance metrics such as latency, throughput, and system resource utilization. The scalable, high-performance architecture of the XE9680 and R760xa servers stands out as a powerful solution for advancing AI capabilities in enterprise data centers, fostering innovation, enhancing data security, and providing a competitive edge.
These results provided valuable insights into the importance of selecting the right hardware and configurations for specific AI tasks. Organizations can leverage the high performance and scalability of the XE9680 and R760xa servers in their AI deployments, ensuring optimal resource utilization and maintaining superior performance. Understanding the impact of different quantization methods enables better decisions for AI model optimization, leading to faster processing times and enhanced efficiency.
Baseline tests revealed that both the XE9680 and R760xa servers efficiently handle Llama 3 8B and 70B models, laying a solid foundation for more complex deployments.
Scaling up multiple instances of Llama 3 8B models on the XE9680 server showed a consistent increase in throughput with manageable latency. The R760xa server demonstrated significant improvements in throughput with increased instances, proving its capability for moderate to high workloads.
Deploying Llama 3 8B models across a Kubernetes cluster of two R760xa servers showcased effective load distribution and scalability. The cluster setup achieved substantial throughput while maintaining latency within acceptable limits, highlighting the benefits of clustered environments for AI workloads.
Running a mix of Llama 3 70B and 8B models on the XE9680 server utilized all available GPUs effectively without significant performance degradation, demonstrating its suitability for diverse AI tasks requiring multiple models.
Tests comparing FP16 and FP8 quantization revealed that FP8 offers better performance in terms of lower latency and higher throughput. The XE9680 server showed significant improvements with FP8 quantization, making it an attractive option for applications needing rapid inference and high efficiency.
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Author: Benjamin Gordy, Tiffany Fahmy, Dell AI Technical Marketing Team