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As we speak, we’re saying the final availability of Amazon Elastic Compute Cloud (Amazon EC2) P6-B200 cases powered by NVIDIA B200 to handle buyer wants for prime efficiency and scalability in synthetic intelligence (AI), machine studying (ML), and excessive efficiency computing (HPC) purposes.
Amazon EC2 P6-B200 cases speed up a broad vary of GPU-enabled workloads however are particularly well-suited for large-scale distributed AI coaching and inferencing for basis fashions (FMs) with reinforcement studying (RL) and distillation, multimodal coaching and inference, and HPC purposes comparable to local weather modeling, drug discovery, seismic evaluation, and insurance coverage threat modeling.
When mixed with Elastic Cloth Adapter (EFAv4) networking, hyperscale clustering by EC2 UltraClusters, and superior virtualization and safety capabilities by AWS Nitro System, you may prepare and serve FMs with elevated velocity, scale, and safety. These cases additionally ship as much as two occasions the efficiency for AI coaching (time to coach) and inference (tokens/sec) in comparison with EC2 P5en cases.
You may speed up time-to-market for coaching FMs and ship sooner inference throughput, which lowers inference value and helps enhance adoption of generative AI purposes in addition to elevated processing efficiency for HPC purposes.
EC2 P6-B200 cases specs
New EC2 P6-B200 cases present eight NVIDIA B200 GPUs with 1440 GB of excessive bandwidth GPU reminiscence, fifth Era Intel Xeon Scalable processors (Emerald Rapids), 2 TiB of system reminiscence, and 30 TB of native NVMe storage.
Listed below are the specs for EC2 P6-B200 cases:
| Occasion measurement | GPUs (NVIDIA B200) | GPU reminiscence (GB) |
vCPUs | GPU Peer to look (GB/s) | Occasion storage (TB) | Community bandwidth (Gbps) | EBS bandwidth (Gbps) |
| P6-b200.48xlarge | 8 | 1440 HBM3e | 192 | 1800 | 8 x 3.84 NVMe SSD | 8 x 400 | 100 |
These cases characteristic as much as 125 p.c enchancment in GPU TFLOPs, 27 p.c enhance in GPU reminiscence measurement, and 60 p.c enhance in GPU reminiscence bandwidth in comparison with P5en cases.
P6-B200 cases in motion
You need to use P6-B200 cases within the US West (Oregon) AWS Area by way of EC2 Capability Blocks for ML. To order your EC2 Capability Blocks, select Capability Reservations on the Amazon EC2 console.
Choose Buy Capability Blocks for ML after which select your complete capability and specify how lengthy you want the EC2 Capability Block for p6-b200.48xlarge cases. The full variety of days which you could reserve EC2 Capability Blocks is 1-14 days, 21 days, 28 days, or multiples of seven as much as 182 days. You may select your earliest begin date for as much as 8 weeks upfront.
Now, your EC2 Capability Block can be scheduled efficiently. The full value of an EC2 Capability Block is charged up entrance, and the value doesn’t change after buy. The fee can be billed to your account inside 12 hours after you buy the EC2 Capability Blocks. To study extra, go to Capability Blocks for ML within the Amazon EC2 Consumer Information.
When launching P6-B200 cases, you should utilize AWS Deep Studying AMIs (DLAMI) to help EC2 P6-B200 cases. DLAMI supplies ML practitioners and researchers with the infrastructure and instruments to shortly construct scalable, safe, distributed ML purposes in preconfigured environments.
To run cases, you should utilize AWS Administration Console, AWS Command Line Interface (AWS CLI) or AWS SDKs.
You may combine EC2 P6-B200 cases seamlessly with numerous AWS managed companies comparable to Amazon Elastic Kubernetes Providers (Amazon EKS), Amazon Easy Storage Service (Amazon S3), and Amazon FSx for Lustre. Assist for Amazon SageMaker HyperPod can also be coming quickly.
Now obtainable
Amazon EC2 P6-B200 cases can be found as we speak within the US West (Oregon) Area and could be bought as EC2 Capability blocks for ML.
Give Amazon EC2 P6-B200 cases a attempt within the Amazon EC2 console. To study extra, seek advice from the Amazon EC2 P6 occasion web page and ship suggestions to AWS re:Submit for EC2 or by way of your normal AWS Assist contacts.
— Channy
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