In real-world vector search performance tests, Intel® Xeon® server architectures outperform AMD EPYC processors when running two commonly used vector search frameworks.
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Intel® Xeon® Processors Set the Standard for Vector Search Benchmark Performance
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From Gold Rush to Factory: How to Think About TCO for Enterprise AI
Less Gold Rush and more Boring Factory – The evolving AI mindset.
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A Practical Guide to CPU-Optimized LLM Deployment on Intel® Xeon® 6 Processors on AWS.
Deploying large language models no longer requires expensive GPUs or complex infrastructure. In this guide, we show how Intel® Xeon® 6 processors paired with vLLM deliver high‑throughput, production‑ready LLM inference entirely on CPUs. Learn how to launch a scalable, OpenAI‑compatible endpoint on AWS Marketplace – complete with NUMA‑aware parallelism, BF16 acceleration, chunked prefill, and optimized KV‑cache performance – so you can run enterprise‑grade LLM workloads at a fraction of traditional GPU costs.
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Bringing Polish AI to Life: Running Bielik LLMs Natively on Intel® Gaudi® 3 Accelerators
From community curiosity to real-world inference – showing how local language models run with day-zero Intel hardware support.
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Optimizing SLMs on Intel® Xeon® Processors: A llama.cpp Performance Study
In this post, we’ll dicuss how to run responsive, CPU-only applications using a quantized SLM in the GPT-Generated Unified Format (GGUF).
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Intel® Xeon® 6 Processors: The Smart Total Cost of Ownership Choice
The latest Intel® Xeon® 6 processors deliver performance advantages across key enterprise workloads, enabling companies to deploy fewer servers and still deliver a similar aggregate performance level compared to AMD EPYC solutions
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Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
Intel® Xeon® processors can deliver a CPU-first platform built for modern AI workloads without added complexity or overhead.
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Document Summarization: Transforming Enterprise Content with Intel® AI for Enterprise RAG
Transform enterprise documents into insights with Document Summarization, optimized for Intel® Xeon® and Intel® Gaudi® with automated NUMA-aware scheduling.
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AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound
We are thrilled to announce an official collaboration between SGLang and AutoRound, enabling low-bit quantization for efficient LLM inference.
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In-production AI Optimization Guide for Xeon: Search and Recommendation Use Case
In this guide, you’ll learn multiple aspects of optimizing the Search and Recommendation model deployed in Production using Intel Xeon CPU servers.
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