Sam Werner’s Post

2025 will be a big year for IBM Power. We intend to integrate the IBM Spyre Accelerator with Power11 and develop a generative AI code assistant for IBM i and RPG. Read more on what is to come in the blog from Bargav Balakrishnan: https://lnkd.in/g5Q6Kmh2

Arm Neoverse-based AWS Graviton is boosting machine learning workloads with the support of Databricks ML Runtime. Graviton3 instances boost XGBoost, LightGBM, and Spark MLlib with up to 50% faster performance. Plus, see how enabling Photon on Databricks ML Runtime delivers a 3.1X improvement. You can learn more about the cost benefits and performance gains that Graviton3 brings to ML workflows in the blog linked below.

#Improved_AI_Performance: IBM Power10 hardware comes with features optimally suited for AI workloads including an #in-core_accelerator called Matrix Math Accelerator (#MMA). Together with the #large_memory_capacity of #IBM #Power10 and high parallelism, these differentiators offer efficient and cost-effective acceleration for AI workloads. For large language models (LLMs), clients can process up to 42% more batch queries per second on IBM Power S1022 servers than compared x86 servers during peak load of 40 concurrent users4 and enjoy inferencing latency below 1 second5. https://lnkd.in/gHkAfu6V

S3: A Game-Changer for Storing Tabular Data at Scale In the world of data programs, scalability, cost-efficiency, and performance are non-negotiabl and Amazon S3 delivers on all fronts. It's not just a storage service; it's the foundation for game-changing data architectures. With features like: - Columnar file formats (Parquet, ORC) for faster queries and reduced storage costs. - Seamless integration with tools like Athena, Redshift Spectrum, and EMR for analytics on the fly. - Infinite scalability and high durability, enabling us to handle petabytes of data with confidence. S3 has redefined how we store and process tabular data. It's no longer just about storage—it's about building a future-ready ecosystem for real-time insights, AI/ML pipelines, and business intelligence at scale. If your data programs still rely on legacy systems, it might be time to rethink. S3 isn't just a service it's a game-changer.

How and why to run machine learning workloads on Kubernetes by via WhatIs: RSS Feed URL: https://ift.tt/wFExUqP

I love hearing how IBM is leaning into AI and the many possibilities it can generate.

Are you considering how you can implement massively scalable storage to handle the data challenges driven by AI? Data is growing rapidly, in more locations and in more formats. To help our clients combat these challenges means modernizing technology and assisting clients in adopting cloud-native architectures. IBM Storage Ceph is an IBM-supported distribution of the open-source Ceph platform that provides massively scalable object, block, and file storage in a single system. It is part of the IBM Storage portfolio of software-defined storage. Learn more about the key role IBM Storage Ceph plays in this effort.

Introduction to the Parameter Server Framework for Distributed Machine Learning https://lnkd.in/dbDFkDHd

Parallelstore is now GA, fueling the next generation of AI and HPC workloads Parallelstore combines a distributed metadata and key-value architecture to deliver high-performance throughput and IOPS for HPC and AI workloads. Read mode on following blog post!

And this is how we do it -> *IBM watsonx.data with Presto C++ v0.286 and query optimizer on IBM Storage Fusion HCI, tested internally by IBM, was able to deliver better price performance compared to Databrick’s Photon engine, with equal query runtime at less than 60% of the cost,derived from public 100 TB TPC-DS Query benchmarks.* So proud of the team that helped to deliver this Hamid Pirahesh Ashok Kumar Yiqun (Ethan) Zhang Jason Sizto Sudheesh SK and many more. #watsonx #data #AI #IBM #HCI #benchmarks #query #optimisation #THINK