Large Language Models (LLMs) deploying on real-world applications presents unique challenges, particularly in terms of computational resources, latency, and cost-effectiveness. In this comprehensive guide, we'll explore...
As transformer models grow in size and complexity, they face significant challenges in terms of computational efficiency and memory usage, particularly when dealing with long sequences....
The field of artificial intelligence (AI) has witnessed remarkable advancements in recent years, and at the heart of it lies the powerful combination of graphics processing...
Large language models (LLMs) like GPT-4, Bloom, and LLaMA have achieved remarkable capabilities by scaling up to billions of parameters. However, deploying these massive models for...
In today's era of rapid technological advancement, Artificial Intelligence (AI) applications have become ubiquitous, profoundly impacting various aspects of human life, from natural language processing to...
Graphics processing unit (GPU) hosting is the use of powerful GPUs in a data center or cloud environment to provide on-demand access to high-performance computing resources....
Researchers from Poland and Japan, working with Sony, have found evidence that machine learning systems trained on GPUs rather than CPUs may contain fewer errors during...
Omri Geller is the CEO and Co-Founder at Run:AI Run:AI virtualizes and accelerates AI by pooling GPU compute resources to ensure visibility and, ultimately, control over...
Ludovic Larzul. is the founder and CEO of Mipsology, a groundbreaking startup focused on state-of-the-art acceleration for deep learning inference. They've devised technology to accelerate the...
Computer scientists from Rice University, along with collaborators from Intel, have developed a more cost-efficient alternative to GPU. The new algorithm is called “sub-linear deep learning...