|CUDA Parallel Processing cores||2304|
|NVIDIA Tensor Cores||288|
|NVIDIA RT Cores||36|
|Frame Buffer Memory||8 GB GDDR6|
|Rays Cast||8 Giga Rays/Sec|
|Peak Single Precision (FP32) Performance||7.1 TFLOPS|
|Peak Half Precision (FP16) Performance||14.2 TFLOPS|
|Peak Integer Operation (INT8) Performance||28.5 TOPS|
|Deep Learning TeraFLOPS1||57.0 TFLOPS|
|Memory Bandwidth||Up to 416 GB/s|
|Max Power Consumption||160 W|
|Graphics Bus||PCI Express 3.0 x 16|
|Display Connectors||DP 1.4 (3) + VirtualLink (1)|
|Form Factor||4.4” H x 9.5” L|
|Product Weight||479 g|
|NVIDIA® 3D Vision®and 3D Vision Pro||Support via 3 pin mini DIN|
|Frame Lock||Compatible (with Quadro Sync II)|
|Power Connector||8-pin PCIe|
2 This feature requires implementation by software applications and is not a stand-alone utility. Please contact firstname.lastname@example.org for details on availability.
Quadro RTX 4000 combines the NVIDIA Turing GPU architecture with the latest memory and display technologies, to deliver the best performance and features in a single-slot PCI-e form factor. Enjoy greater fluidity with photorealistic rendering, experience faster performance with AI-enabled applications and create detailed, lifelike VR experiences more cost-effectively and across a broader range of workstation chassis configurations.
Dramatically reduce visual aliasing artifacts or "jaggies" with up to 64X FSAA (128X with SLI) for unparalleled image quality and highly realistic scenes.
Texture from and render to 32K x 32K surfaces to support applications that demand the highest resolution and quality image processing./p>
Synchronize the display and image output of up to 32 displays from 8 GPUs (connected through two Sync II boards) in a single system, reducing the number of machines needed to create an advanced video visualization environment.
Deep learning frameworks such as Caffe2, MXNet, CNTK, TensorFlow, and others deliver dramatically faster training times and higher multi-node training performance. GPU accelerated libraries such as cuDNN, cuBLAS, and TensorRT delivers higher performance for both deep learning inference and High-Performance Computing (HPC) applications.
Natively execute standard programming languages like C/C++ and Fortran, and APIs such as OpenCL, OpenACC and Direct Compute to accelerates techniques such as ray tracing, video and image processing, and computation fluid dynamics.
A single, seamless 49-bit virtual address space allows for the transparent migration of data between the full allocation of CPU and GPU memory.
GPUDirect for Video speeds communication between the GPU and video I/O devices by avoiding unnecessary system memory copies and CPU overhead.
Maximize system uptime, seamlessly manage wide-scale deployments and remotely control graphics and display settings for efficient operations.