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100% of Blog Posts at Red Hat's Official Blog Are Promotion of Microsoft and Slop Plagiarism
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Red Hat Official ☛ Modernizing Microsoft SQL Server: Choosing the right path with Red Hat [Ed: As usual, instead of trying to sell free software for database Red Hat helps Microsoft sell proprietary spyware]
Each route solves a different problem. They do not have to be treated as competing or mutually exclusive choices. In other words, you don’t need to commit to a single option. Exploring one option doesn't lock you in forever, and you can iterate through them depending on where you are in the customer journey.
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Red Hat Official ☛ From fine-tuned model to cheaper and faster inference: Speculator training on Red Hat OpenShift AI with Kubeflow [Ed: Instead of selling Linux, IBM compels Red Hat to promote a scam, a pyramid scheme and hype]
Now it's deployed in production, serving real users, and burning through GPU hours at $2 to $5 per hour per NVIDIA H100 GPU. And here's the part most organizations don't realize until the invoice arrives: 70 to 80 percent or more of enterprise AI spending goes to inference, not training. The model is done learning. The ongoing cost is answering questions.
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Red Hat Official ☛ From bare metal to diverse AI revenue streams: Navigating the GPU cloud platform challenge [Ed: Red Hat has been reduced into a key participant in this pyramid scheme]
This strategic importance is underscored by recent multibillion dollar agreements, such as Nebius’s $17.4 billion deal with Microsoft and CoreWeave’s infrastructure partnerships with Microsoft and OpenAI. These deals reveal a structural reliance where tech giants use neoclouds as dedicated third-party GPU infrastructure proxies to bridge capacity gaps, rather than adopting native platforms. These cloud services require multi-tenant, security-focused, and elastic infrastructure that can be consumed as an API-driven resource. Red Hat platforms are specifically designed to accelerate this transformation.
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Red Hat Official ☛ AutoRAG advances in Red Hat OpenShift AI 3.5 [Ed: IBM Red Hat selling slop, not Linux]
AutoRAG reduces that guesswork by automating evaluation and hyperparameter tuning across RAG frameworks. Built on Kubeflow Pipelines and driven by the open source IBM ai4rag optimization engine, the upcoming advanced technical preview of AutoRAG in Red Hat OpenShift AI 3.5 bridges the gap between automated experimentation and scalable enterprise deployment.
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Red Hat ☛ Understanding W8A8 INT8 LLM quantization: Accuracy and performance results [Ed: Never ending promotion of plagiarism engines at Red Hat these days]
In Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy, we compressed a Llama 3.1 8B Instruct model from 14.9 GB to 8.0 GB using 8-bit integer (INT8) W8A8 quantization with SmoothQuant and Generative Pre-trained Transformer Quantization (GPTQ).