Artificial intelligence (AI) is evolving rapidly, with new neural network models, techniques, and use cases emerging regularly. While there is no single architecture that works best for all machine and deep learning applications, FPGAs can offer distinct advantages over GPUs and other types of hard...
In a surprising twist, Intel announced on Thursday that its Falcon Shores GPU for AI and HPC applications will not be released to the market but will remain an internal test processor to develop the hardware and software foundations for its successor, codenamed Ja...
As the volume of online videos continues to grow exponentially, demand for solutions to efficiently search and gain insights from video continues to grow as well. T4 delivers extraordinary performance for AI video applications, with dedicated hardware transcoding engines that bring twice the decoding pe...
NVIDIA Jarvis:一个GPU加速对话人工智能应用的框架 Introducing NVIDIA Jarvis: A Framework for GPU-Accelerated Conversational AI Applications 实时会话人工智能是一项复杂而富有挑战性的任务。为了允许与最终用户进行实时、自然的交互,模型需要在300毫秒内完成计算。自然的相互作用具有挑战性,需要多模态的感觉整合。模型管...
secure NVIDIA networking into enterprise data center servers, built and sold by NVIDIA’s OEM partners. This program enables customers to identify, acquire, and deploy systems for traditional and diverse modern AI applications from the NVIDIA NGC catalog on a single high-performance, cost-effective,...
while the pace of improvements in CPUs, which are designed to process a wider range of programming, has fallen behind. So even if chip manufacturers can’t pack silicon more densely with transistors, chip designers may be able tocontinueoptimizing to improve the price/performance ratio for AI....
GPU for Machine Learning Some of the most exciting applications for GPU technology involve AI and machine learning. Because GPUs incorporate an extraordinary amount of computational capability, they can deliver incredible acceleration in workloads that take advantage of the highly parallel nature of GPUs...
“Heterogeneous compute is the key to delivering the processing performance required for AI-powered applications,” said Charles Xie, creator of the Milvus project and CEO, Zilliz. “With Milvus’s NVIDIA GPU support andRAFT[3]-based integration, that capability is now available at massive scale ...
Hub of AI frameworks including PyTorch and TensorFlow, SDKs, AI models, Jupyter Notebooks, Model Scripts, and HPC applications.
Accelerate AI training, power complex simulations, and render faster with NVIDIA H100 GPUs on Paperspace. Easy setup, cost-effective cloud compute.