Hypertec Group’s Post

GPU cluster reliability is critical for AI/ML workloads, yet challenges persist. Even Meta faced significant hardware issues during their Llama 3.1 training. At Together AI, we've developed a robust validation framework for our GPU clusters before we deploy them to our cloud. Our new blog post covers our rigorous acceptance testing process, including: - Preparing and configuring GPU clusters - Tools used for stress testing - Network performance testing and storage validation - Model use case testing - Observability and continuous monitoring for hardware failures The insights should be useful for any engineering or infrastructure teams looking to deploy AI/ML workloads. Read more here: https://lnkd.in/eNEG8NsC

Yesterday, we announced an expanded collaboration with NVIDIA to deliver sovereign #artificialintelligence to customers around the world. The combination of NVIDIA’s full-stack AI platform with Oracle’s Enterprise #AI– deployable across OCI Dedicated Region, Oracle Alloy, Oracle EU Sovereign Cloud, and Oracle Government Cloud – offers customers a state-of-the-art AI solution that provides greater control over operations, location, and security to help support digital sovereignty. Oracle also plans to take advantage of the latest NVIDIA Grace Blackwell computing platform across OCI Supercluster, OCI Compute, and NVIDIA DGX Cloud on OCI. #GTC24 https://lnkd.in/gPfm9Pqq    

GPU cluster reliability is critical for AI/ML workloads, yet challenges persist. Even Meta faced significant hardware issues during their Llama 3.1 training. At Together AI, we've developed a robust validation framework for our GPU clusters before we deploy them to our cloud. Our new blog post covers our rigorous acceptance testing process, including: - Preparing and configuring GPU clusters - Tools used for stress testing - Network performance testing and storage validation - Model use case testing - Observability and continuous monitoring for hardware failures The insights should be useful for any engineering or infrastructure teams looking to deploy AI/ML workloads. Read more here: https://lnkd.in/eNEG8NsC

What's unusual about this picture? It's the smarts you can't see - This is HPE Private Cloud for AI, a turnkey solution built with the NVidia AI platform, HPE AI Essentials curated AI software, HPE Inferencing Servers and AI Storage with a cloud operating model. Designed to make home brew and hyperscaler AI projects manageable by IT departments; deployed where you need it, with the guardrails, automation and robust features you need to take your AI project into production and make it a success. https://lnkd.in/eZQcZuWV #HPE #HPEAI #Privatecloud

On-premises foundation models from Mistral AI are now on watsonx. 🔥 As part of our commitment to make high quality, performant foundation models widely accessible, deployable, and secure for enterprises, this watsonx․ai add-on enables customers to deploy and benefit from Mistral Large 2 directly on their own systems. With this, enterprises can now: ✅ Tune models without needing to move sensitive data to a third-party cloud ✅ Leverage large model performance at a relatively smaller scale ✅ Handle complex tasks like #RAG, non-English languages, and coding Learn more about the powerful combination of IBM, Mistral AI, and the #watsonx platform: https://ibm.co/3XWpfYk

Today, the Toshiba Digital Solutions Corporation team launched SQBM+ for Azure, a version of its SQBM+ software customized for Azure Marketplace, operated by Microsoft Corporation. SQBM+ for Azure joins the version already available on Amazon Web Services, and underlines Toshiba’s determination to make SQBM+ available to customers working in different IT environments. This solution is based on Toshiba’s Simulated Bifurcation Machine, which uses quantum-inspired algorithms to quickly find high-precision, high-quality solutions to complex problems. Read how Toshiba aims to leverage SQBM+ through numerous fields, using the power of quantum computing to resolve complex problems across areas such as high speed, high frequency stock trading, computational drug discovery, energy management, and materials development: https://bit.ly/3SBN5F0 #Toshiba #SQBM #QuantumComputing #Quantum #Azure #AWS

GPU cluster reliability is critical for AI/ML workloads, yet challenges persist. Even Meta faced significant hardware issues during their Llama 3.1 training. At Together AI, we've developed a robust validation framework for our GPU clusters before we deploy them to our cloud. Our new blog post covers our rigorous acceptance testing process, including: - Preparing and configuring GPU clusters - Tools used for stress testing - Network performance testing and storage validation - Model use case testing - Observability and continuous monitoring for hardware failures The insights should be useful for any engineering or infrastructure teams looking to deploy AI/ML workloads. Read more here: https://lnkd.in/eNEG8NsC

GPU cluster reliability is critical for AI/ML workloads, yet challenges persist. Even Meta faced significant hardware issues during their Llama 3.1 training. At Together AI, we've developed a robust validation framework for our GPU clusters before we deploy them to our cloud. Our new blog post covers our rigorous acceptance testing process, including: - Preparing and configuring GPU clusters - Tools used for stress testing - Network performance testing and storage validation - Model use case testing - Observability and continuous monitoring for hardware failures The insights should be useful for any engineering or infrastructure teams looking to deploy AI/ML workloads. Read more here: https://lnkd.in/eNEG8NsC