Large Language Models are revolutionizing AI applications; however, slow inference speeds continue to be a significant challenge. Intel researchers, along with industry and university partners, are actively working to address this issue and accelerate the efficiency of LLMs. In a series of blog posts, Intel Researchers introduce several novel works, including a method that accelerates text generation by up to 2.7 times, a method that extends assisted generation to work with a small language model from any model family, and a technique that enables any small “draft” model to accelerate any LLM, regardless of vocabulary differences
-
-
Articles récents
- Orchestrating AI for Real Business Value: Google Cloud’s Approach to Scalable Intelligence
- Curious Case of Chain of Thought: Improving CoT Efficiency via Training-Free Steerable Reasoning
- Intel Labs Works with Hugging Face to Deploy Tools for Enhanced LLM Efficiency
- AI’s Next Frontier: Human Collaboration, Data Strategy, and Scale
- Efficient PDF Summarization with CrewAI and Intel® XPU Optimization
-
Neural networks news
Intel NN News
- Orchestrating AI for Real Business Value: Google Cloud’s Approach to Scalable Intelligence
In the race to operationalize AI, success hinges not on hype, but on clarity, customization, and […]
- Curious Case of Chain of Thought: Improving CoT Efficiency via Training-Free Steerable Reasoning
Researchers from the University of Texas at Austin and Intel Labs investigated chain-of-thought […]
- AI’s Next Frontier: Human Collaboration, Data Strategy, and Scale
Ramtin Davanlou, CTO of the Accenture and Intel Partnership, explores what it really takes for […]
- Orchestrating AI for Real Business Value: Google Cloud’s Approach to Scalable Intelligence
-