Source URL: https://aws.amazon.com/blogs/aws/new-amazon-ec2-p5en-instances-with-nvidia-h200-tensor-core-gpus-and-efav3-networking/
Source: AWS News Blog
Title: New Amazon EC2 P5en instances with NVIDIA H200 Tensor Core GPUs and EFAv3 networking
Feedly Summary: Amazon EC2 P5en instances deliver up to 3,200 Gbps network bandwidth with EFAv3 for accelerating deep learning, generative AI, and HPC workloads with unmatched efficiency.
AI Summary and Description: Yes
**Summary:** The announcement details the launch of Amazon EC2 P5en instances, showcasing significant advancements in machine learning (ML) performance due to improved hardware specifications, such as NVIDIA H200 Tensor Core GPUs and Intel Xeon processors. It emphasizes enhanced efficiencies for ML training, inference workloads, and high-performance computing applications, including capabilities for generative AI and large language models (LLMs), making it a crucial update for AI and cloud infrastructure professionals.
**Detailed Description:**
– **Launch Announcement:** The document reveals the general availability of Amazon EC2 P5en instances, which are specifically designed for demanding machine learning and computational tasks.
– **Hardware Specifications:**
– **Powerful GPUs:** Utilizes NVIDIA H200 Tensor Core GPUs.
– **Advanced Processors:** Features custom 4th generation Intel Xeon Scalable processors, providing enhanced performance with all-core turbo frequencies reaching 3.8 GHz.
– **Memory Bandwidth:** Offers a 50% increase in memory bandwidth and supports up to four times the throughput between CPU and GPU due to PCIe Gen5 technology.
– **Performance Improvements:**
– **Latency Reduction:** The P5en instances exhibit a 35% latency improvement over the previous generation (P5), which is beneficial for distributed training workloads.
– **Versatile Applications:** Suitable for diverse applications, including:
– Machine learning (ML) training and inference.
– High-performance computing (HPC) tasks.
– Real-time data processing.
– Deep learning and generative AI applications.
– Uses in simulations, pharmaceutical discovery, weather forecasting, and financial modeling.
– **Storage and Network Enhancements:**
– Increased local storage performance by up to two times.
– EBS bandwidth improvements by 25%, contributing to better inference performance for local storage used in caching model weights.
– Supports up to 3200 Gbps of EFA networking bandwidth.
– **Capacity Reservations and Management:**
– Outlines how to reserve EC2 Capacity Blocks for ML, allowing users to plan their capacity effectively.
– Provides pricing structure and methods to purchase capacity blocks up to 8 weeks in advance.
– **Use Cases for ML Practitioners:**
– Encourages using AWS Deep Learning AMIs for deploying applications on P5en instances.
– Suggests running containerized ML applications using AWS Deep Learning Containers with ECS or EKS for better scalability and flexibility.
– Highlights access to vast data storage solutions like Amazon S3 and FSx for Lustre for high throughput and IOPS.
In summary, the launch of Amazon EC2 P5en instances represents a significant leap in cloud computing offerings targeted at AI and ML professionals, particularly those engaged in high-performance system applications. This expansion enables practitioners to build more efficient, responsive, and scalable solutions in the cloud environment.