The Lenovo ThinkSystem SR675 V3 server–powered by NVIDIA’s new L40S GPUs–took the stage at SIGGRAPH in Los Angeles, becoming a focal point in NVIDIA’s plans to advance global AI rollouts and introduce generative AI applications like intelligent chatbots, search, and summarization tools to users across various industries.
Dell Technologies has introduced new offerings to help customers securely build generative AI (GenAI) models on-premises that will accelerate improved outcomes driving new levels of intelligence. Expanding on the Project Helix announcement in May, Dell GenAI Solutions span IT infrastructure, PCs, and professional services. GenAI Solutions from Dell will simplify the adoption of full-stack GenAI
Lenovo and NVIDIA have teamed up to develop an advanced AI and computer vision platform, the Lenovo ThinkEdge SE70. This platform has been designed to transform existing camera infrastructures into intelligent automated environments, making it an ideal solution across diverse industries.
The Intel Habana Gaudi2 deep learning accelerator and 4th Gen Intel Xeon Scalable processors have demonstrated impressive results in the MLPerf Training 3.0 benchmark, according to a press release by the company. The benchmark, published by MLCommons, is a widely recognized industry standard for AI performance.
In the high-stakes arena of enterprise AI, tech titans are in a relentless race to unveil the next significant innovation in hardware. Dell Technologies recently introduced the Dell PowerEdge XE9680 for Generative AI and is flexing it in a recent whitepaper. The PowerEdge XE9680, explicitly designed to tackle AI workloads, is not just a new
The world of Artificial Intelligence is evolving at breakneck speed, blink, and you will miss the next advance. With model sizes getting larger and larger, researchers and developers are constantly seeking ways to improve the efficiency and performance of AI models. One of the easiest ways to achieve this is using multiple Graphics Processing Units
In recent months, large language models have been the subject of extensive research and development, with state-of-the-art models like GPT-4, Meta LLaMa, and Alpaca pushing the boundaries of natural language processing and the hardware required to run them. Running inference on these models can be computationally challenging, requiring powerful hardware to deliver real-time results.
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