The latest GPU performance is always available in the Deep Learning Inference Performance page. Using high-performance optimizations and lower precision inference (FP16 and INT8) you can get dramatically higher performance on GPUs than alternative platforms. The pipeline needs to complete in under 300 ms for the application to appear responsive, placing very tight latency requirements on each model. Applications developed with NVIDIA’s inference SDKs can deliver up to 40x higher inference performance on GPUs versus CPU-only platforms.īuilt on the CUDA unified platform, NVIDIA’s CUDA-X inference solution provides an easy way to take a model developed on your desktop in any framework, apply optimizations and deploy it for inference in the datacenter as well as the edge.Ĭonversational AI and recommendation systems application pipelines execute 20-30 models, each with millions of parameters, for a single customer query.
There are over a hundred repos on NVIDIA Github covering products, demos, samples and tutorials to get started.ĬUDA-X AI includes high performance deep learning inference SDKs that minimize latency and maximize throughput for applications such as computer vision, conversational AI and recommenders in production environments. NVIDIA AI Toolkit includes libraries for transfer learning, fine tuning, optimizing and deploying pre-trained models across a broad set of industries and AI workloads The NVIDIA® NGC™ catalog provides pre-trained models, training scripts, optimized framework containers and inference engines for popular deep learning models. Framework developers and researchers use the flexibility of GPU-optimized CUDA-X AI libraries to accelerate new frameworks and model architectures.īuilt on CUDA-X, NVIDIA’s unified programming model provides a way to develop deep learning applications on the desktop or datacenter, and deploy them to datacenters, resource constrained IoT devices as well as automotive platforms with minimal to no code changes.
CUDA-X AI libraries deliver world leading performance for both training and inference across industry benchmarks such as MLPerf.Įvery deep learning framework including PyTorch, TensorFlow and JAX is accelerated on single GPUs, as well as scale up to multi-GPU and multi-node configurations. The CUDA installation packages can be found on the CUDA Downloads Page.
NVIDIA CUDA TOOLKIT FOR DESKTOP DRIVER
For convenience, NVIDIA includes a compatible CUDA driver with the toolkit.
NVIDIA CUDA TOOLKIT FOR DESKTOP SOFTWARE
NVIDIA CUDA-X AI is a complete deep learning software stack for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision. NVIDIA CUDA Toolkit and compatible CUDA driver is required for CUDALink to work.
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