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Articles récents
- Optimizing SLMs on Intel® Xeon® Processors: A llama.cpp Performance Study
- Intel® Xeon® 6 Processors: The Smart Total Cost of Ownership Choice
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
- Document Summarization: Transforming Enterprise Content with Intel® AI for Enterprise RAG
- AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound
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Neural networks news
Intel NN News
- 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 […]
- Intel® AI for Enterprise Inference as a Deployable Architecture on IBM Cloud
Intel® AI for Enterprise Inference as a Deployable Architecture on IBM CloudAuthored by: Pai […]
- Intel® Xeon® 6 Processors: The Smart Total Cost of Ownership Choice
The latest Intel® Xeon® 6 processors deliver performance advantages across key enterprise […]
- Optimizing SLMs on Intel® Xeon® Processors: A llama.cpp Performance Study
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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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