How programmable switches can handle collective offloading seamlessly. @Juniper Networks' Sharada Yeluri deep dives into the topic as well as the challenges and future trends. https://lnkd.in/gbzN_USP
APNIC’s Post
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Check out this great blog article to learn how Juniper Networks optimizes the ever-present high traffic loads to avoid the slowing effects of congestion and packet drops that would cost customers GPU training cycles and time to market: https://hubs.li/Q029sVyw0
Don’t Let Your AI Get Caught in Traffic | Official Juniper Networks Blogs
https://blogs.juniper.net
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Does your business need to review or assess masses of images to make informed strategic decisions? It’s not all just CPU’s & GPU’s, for very Data intensive workloads FPGA’s (Field Programmable Gate Arrays) will make a big difference. Check out this blog and gain our point of view and assess some use cases. https://lnkd.in/ekjZucxj #iwork4dell
FPGA's - Use Cases in the Data Center | Dell
dell.com
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Naive implementations of transformer architectures are rather inefficient in their use of GPU compute resources. A big part of this is the I/O resources needed to handle the O(N**2) memory footprint. The blog linked below has a helpful explanation of how FlashAttention reduces this to O(N) memory usage and significantly speeds up both training and inference with transformer models. https://lnkd.in/g2vPJq9B
ELI5: Flash Attention
gordicaleksa.medium.com
Technologist and Sr. Director of Engineering @ Juniper Networks
2wThank you APNIC for posting this article.