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Transforming workloads: Harnessing AI within VMware environments

CIO

The networking, compute, and storage needs not to mention power and cooling are significant, and market pressures require the assembly to happen quickly. Infrastructure challenges in the AI era Its difficult to build the level of infrastructure on-premises that AI requires. AI workloads demand flexibility and the ability to scale rapidly.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

Load balancer – Another option is to use a load balancer that exposes an HTTPS endpoint and routes the request to the orchestrator. You can use AWS services such as Application Load Balancer to implement this approach. As a result, building such a solution is often a significant undertaking for IT teams.

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Host concurrent LLMs with LoRAX

AWS Machine Learning - AI

Furthermore, LoRAX supports quantization methods such as Activation-aware Weight Quantization (AWQ) and Half-Quadratic Quantization (HQQ) Solution overview The LoRAX inference container can be deployed on a single EC2 G6 instance, and models and adapters can be loaded in using Amazon Simple Storage Service (Amazon S3) or Hugging Face.

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Grid modernization: A strategic guide for energy sector CIOs

CIO

The shift toward a dynamic, bidirectional, and actively managed grid marks a significant departure from traditional grid architecture. This transformation is fueled by several factors, including the surging demand for electric vehicles (EVs) and the exponential growth of renewable energy and battery storage.

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Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight

AWS Machine Learning - AI

MaestroQA integrated Amazon Bedrock into their existing architecture using Amazon Elastic Container Service (Amazon ECS). The customer interaction transcripts are stored in an Amazon Simple Storage Service (Amazon S3) bucket. The following architecture diagram demonstrates the request flow for AskAI.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. In the following sections, we explain how to deploy this architecture.

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NeuReality lands $35M to bring AI accelerator chips to market

TechCrunch

“ NeuReality was founded with the vision to build a new generation of AI inferencing solutions that are unleashed from traditional CPU-centric architectures and deliver high performance and low latency, with the best possible efficiency in cost and power consumption,” Tanach told TechCrunch via email. Image Credits: NeuReality.

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