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Articles récents
- Reduce Downtime Up To 50% by Utilizing AI-Ready RAS Features of Intel® Xeon® Processors
- How to Fine-Tune an LLM on Intel® GPUs With Unsloth
- Intel® Xeon® Processors Set the Standard for Vector Search Benchmark Performance
- From Gold Rush to Factory: How to Think About TCO for Enterprise AI
- A Practical Guide to CPU-Optimized LLM Deployment on Intel® Xeon® 6 Processors on AWS.
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Neural networks news
Intel NN News
- Reduce Downtime Up To 50% by Utilizing AI-Ready RAS Features of Intel® Xeon® Processors
As generative and agentic AI use cases proliferate across nearly every industry, improving the […]
- How to Fine-Tune an LLM on Intel® GPUs With Unsloth
Fine-tuning an LLM doesn’t have to require massive infrastructure. With Unsloth now supporting […]
- Intel® Xeon® Processors Set the Standard for Vector Search Benchmark Performance
In real-world vector search performance tests, Intel® Xeon® server architectures outperform AMD […]
- Reduce Downtime Up To 50% by Utilizing AI-Ready RAS Features of Intel® Xeon® Processors
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Archives mensuelles : janvier 2023
Getting started with classical Machine Learning Frameworks using Google Colaboratory
Installing major machine learning frameworks optimized by Intel on Google Colaboratory (Colab)
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Training Causal Language Models on SDSC’s Gaudi-based Voyager Supercomputing Cluster
The SDSC Voyager supercomputer is an innovative AI system designed specifically for science and engineering research at scale. Funded by the National Science Foundation, Voyager represents a collaboration with the San Diego Supercomputer Center at UC San Diego, Supermicro, and Intel’s Habana Labs, facilitating … Continuer la lecture
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Top 10 Intel Labs Posts of 2022
Intel Labs presented many exciting research innovations in 2022. Read for a brief description of Intel Labs’ top 10 blogs for 2022.
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Intel Labs at The Winter Conference on Applications of Computer Vision
Intel presents five computer vision papers that detail novel works that include a Dynamic Scene Graph Detection Transformer, a Fast Learnable Once-for-all Adversarial Training method, a method for quantizing convolutional neural networks for efficient training, face access models applied in a hypothetical social network, and a … Continuer la lecture
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