{"title":"PNY Technologies","description":"","products":[{"product_id":"pny-nvidia-rtx-4000-ada-professional-graphics-card","title":"PNY NVIDIA RTX 4000 ADA Professional Graphics Card","description":"\u003cdiv class=\"row contained h-pushLgTop centered-med\"\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch2 class=\"h-h1 h-colorWhite h-caps\"\u003eNVIDIA RTX 4000 ADA GENERATION\u003c\/h2\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA Ada Lovelace Architecture\u003c\/h4\u003e\n\u003cp\u003eExperience fast, interactive performance—powered by the latest NVIDIA Ada Lovelace architecture-based GPU—with ultra-fast, onboard graphics memory technology and optimized software drivers for professional applications. The Shader Execution Reordering (SER) system allows on-the-fly organization and reordering of workloads, grouping similar performing threads so the streaming multiprocessor (SM) and RT Core can operate more efficiently.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eCUDA Cores\u003c\/h4\u003e\n\u003cp\u003eThe NVIDIA Ada Lovelace architecture-based CUDA cores deliver up to 2X the single-precision floating-point (FP32) throughput compared to the previous generation, providing significant performance improvements for graphics workflows such as 3D model development and compute for workloads like desktop simulation for CAE.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eThird-Generation RT Cores\u003c\/h4\u003e\n\u003cp\u003eThird-generation RT Cores provide up to 2X the throughput of the previous generation and the ability to run ray tracing with either shading or denoising capabilities concurrently. This accelerates renders for M\u0026amp;E content creation, AECO design evaluations, and manufacturing virtual prototyping. Third-generation RT Cores deliver up to 2X the ray-tracing performance over the previous generation, delivering groundbreaking performance for photorealistic rendering. Enhanced RT Cores combined with new SER technology dynamically reorder inefficient workloads, dramatically improving shader performance to accelerate end-to-end ray-traced image rendering performance.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row contained centered-med\"\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eFourth Generation Tensor Cores\u003c\/h4\u003e\n\u003cp\u003eFourth-generation Tensor Cores provide up to 4X faster AI training performance than the previous generation with FP16 precision. Support for the new FP8 data format for inference provides more than 4X faster performance than the previous generation and reduces data memory usage by half (compared to the FP16 data format).\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003ePCIe Gen 4\u003c\/h4\u003e\n\u003cp\u003eRTX 4000 supports PCIe Gen4, which doubles the bandwidth of PCIe Gen3 from 15.75GB\/sec to 31.5 GB\/sec for x16 connections, improving data transfer speeds from CPU memory for data-intensive tasks such as AI, data science, and creating 3D models from large datasets. Faster PCIe performance also accelerates GPU DMA transfers, providing faster video data transfers from GPUDirect for Video-enabled devices and faster IO with GPUDirect Storage.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eHigher Speed GDDR6 Memory\u003c\/h4\u003e\n\u003cp\u003eRTX 4000 features 20GB of GDDR6 memory, providing the memory needed for rendering, data science, engineering simulation, and other GPU memory-intensive applications. Support for ECC memory delivers uncompromised computing accuracy and reliability for mission-critical applications.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row contained centered-med\"\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eMulti-Display\u003c\/h4\u003e\n\u003cp\u003eFour mini-DisplayPort 1.4a connectors, support for multiple 8K monitors, NVIDIA Quadro Sync, Mosaic, and Warp and Blend enable massive immersive environments for CAVE, video walls, and location-based entertainment deployments.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eExtended Reality\u003c\/h4\u003e\n\u003cp\u003eSupport for the latest high-resolution HMD devices, high-performance graphics, and 20GB of GPU memory enable the creation of AR and VR experiences for training, product validations, building walkthroughs, and compelling entertainment.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eForm Factor and Power Efficiency\u003c\/h4\u003e\n\u003cp\u003eWith a single-slot, low-profile form factor, and power-efficient design, RTX 4000 fits into a wide range of workstation chassis, providing professionals with a generous selection of compatible workstations from worldwide OEM vendors and delivering a groundbreaking GPU architecture for AI and AI-based neural graphics in desktop workstations.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row contained centered-med h-pushLgBottom\"\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eMotion BVH\u003c\/h4\u003e\n\u003cp\u003eHardware-accelerated rendering of motion blur—a common cinematic effect that's difficult to render—means artists no longer need to rely on traditional methods of using motion vectors to achieve motion blur. Motion vectors allow the artist to adjust motion blur in post but require visual fixes for reflections and translucency.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA DLSS\u003c\/h4\u003e\n\u003cp\u003eThe Ada Lovelace GPU architecture features a new Optical Flow Accelerator and AI-based DLSS Super Resolution with DL denoiser that boost DLSS 3.0's frame rates up to 4X compared to the previous version while maintaining or exceeding native image quality.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA RTX Broadcast Engine\u003c\/h4\u003e\n\u003cp\u003eThe NVIDIA RTX Broadcast Engine transforms offices into broadcast studios, upgrading standard webcams and microphones into premium smart devices with the power of AI. Improve the video and audio quality of live streams with AI capabilities such as virtual backgrounds, webcam auto frame, and microphone noise removal. With dedicated AI processors called Tensor Cores on NVIDIA RTX GPUs, the AI networks can run in real-time alongside your applications.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"feature-name\"\u003e\n\u003ch3 class=\"h-h4 h-colorWhite\"\u003e\n\u003ci class=\"icon-tools icon--empty\"\u003e\u003c\/i\u003eSOFTWARE SUPPORT\u003c\/h3\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row contained centered-med h-pushLgTop\"\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA RTX Experience\u003c\/h4\u003e\n\u003cp\u003eNVIDIA RTX Experience delivers a suite of productivity tools to your desktop workstation, including desktop recording in up to 8K, automatic alerts for the latest NVIDIA RTX Enterprise driver updates, and access gaming features. The application is available for\u003cspan\u003e \u003c\/span\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/design-visualization\/software\/rtx-experience\/\" data-mce-href=\"https:\/\/www.nvidia.com\/en-us\/design-visualization\/software\/rtx-experience\/\" target=\"_blank\"\u003edownload here\u003c\/a\u003e\u003c\/strong\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA CUDA Parallel Computing Platform\u003c\/h4\u003e\n\u003cp\u003eNatively execute standard programming languages like C\/C++ and Fortran, and APIs such as OpenCL, OpenACC, and Direct Compute to accelerate techniques such as ray tracing, video and image processing, and computation fluid dynamics.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eUnified Memory\u003c\/h4\u003e\n\u003cp\u003eA single, seamless 49-bit virtual address space allows for the transparent migration of data between the full allocation of CPU and GPU memory.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row contained centered-med h-pushLgBottom\"\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA GPU Direct for Video\u003c\/h4\u003e\n\u003cp\u003eGPUDirect for Video speeds communication between the GPU and video I\/O devices by avoiding unnecessary system memory copies and CPU overhead.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"column med-8 h-pushBottom\"\u003e\n\u003ch4 class=\"h-h6 h-pushSmBottom\"\u003eNVIDIA Enterprise Management Tools\u003c\/h4\u003e\n\u003cp\u003eMaximize system uptime, seamlessly manage wide-scale deployments, and remotely control graphics and display settings for efficient operations.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003ctable class=\"table h-textCenter\" data-mce-fragment=\"1\"\u003e\n\u003ctbody data-mce-fragment=\"1\"\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eProduct\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eNVIDIA RTX 4000 Ada Generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eArchitecture\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eNVIDIA Ada Lovelace Architecture\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eFoundry\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eTSMC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eProcess Size\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e4 nm NVIDIA Custom Process\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eTransistors\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e35.8 billion\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eDie Size\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e294.5 mm\u003csup data-mce-fragment=\"1\"\u003e2\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eCUDA Parallel Processing Cores\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e6,144\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eNVIDIA Tensor Cores\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e192\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eNVIDIA RT Cores\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e48\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eSingle-Precision Performance\u003csup data-mce-fragment=\"1\"\u003e1\u003c\/sup\u003e\n\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e26.7 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eRT Core Performance\u003csup data-mce-fragment=\"1\"\u003e1\u003c\/sup\u003e\n\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e61.8 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eTensor Performance\u003csup data-mce-fragment=\"1\"\u003e1\u003c\/sup\u003e\n\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e327.6 TFLOPS\u003csup data-mce-fragment=\"1\"\u003e2\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eGPU Memory\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e20 GB GDDR6 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eMemory Interface\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e160-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eMemory Bandwidth\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e360 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eMax Power Consumption\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e130W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eGraphics Bus\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003ePCI Express 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eDisplay Connectors\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eDP 1.4a (4)\u003csup data-mce-fragment=\"1\"\u003e3\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eForm Factor\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e4.4\" (H) x 9.5\"(L) Single Slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eProduct Weight\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e528 g\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eThermal Solution\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eBlower Active Fan\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eNVIDIA 3D Vision\u003csup data-mce-fragment=\"1\"\u003e®\u003c\/sup\u003e\u003cspan data-mce-fragment=\"1\"\u003e \u003c\/span\u003eand 3D Vision Pro\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eSupport via 3-pin mini DIN\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eFrame Lock\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eCompatible (with Quadro Sync II)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eNVLink\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003eNot Supported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003cth scope=\"row\" data-mce-fragment=\"1\"\u003e\n\u003ch4 class=\"h-h6\" data-mce-fragment=\"1\"\u003eNVENC | NVDEC\u003c\/h4\u003e\n\u003c\/th\u003e\n\u003ctd class=\"h-colorGrey\" data-mce-fragment=\"1\"\u003e2x | 2x (+AV1 Encode \u0026amp; Decode)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003ctfoot data-mce-fragment=\"1\"\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003ctd class=\"h-softSm h-textCenter\" colspan=\"2\" data-mce-fragment=\"1\"\u003e\n\u003cp data-mce-fragment=\"1\"\u003e\u003cstrong data-mce-fragment=\"1\"\u003ePerformance numbers may be subject to change until product availability.\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr data-mce-fragment=\"1\"\u003e\n\u003ctd class=\"h-softSm h-colorGrey\" colspan=\"2\" data-mce-fragment=\"1\"\u003e\n\u003cul class=\"h-hardLeft\" data-mce-fragment=\"1\"\u003e\n\u003cli data-mce-fragment=\"1\"\u003e\u003csmall data-mce-fragment=\"1\"\u003e\u003csup data-mce-fragment=\"1\"\u003e1\u003c\/sup\u003e\u003cspan data-mce-fragment=\"1\"\u003e \u003c\/span\u003ePeak rates are based on GPU boost clock.\u003c\/small\u003e\u003c\/li\u003e\n\u003cli data-mce-fragment=\"1\"\u003e\u003csmall data-mce-fragment=\"1\"\u003e\u003csup data-mce-fragment=\"1\"\u003e2\u003c\/sup\u003e\u003cspan data-mce-fragment=\"1\"\u003e \u003c\/span\u003eEffective FP8 TFLOPS using the new sparsity feature.\u003c\/small\u003e\u003c\/li\u003e\n\u003cli data-mce-fragment=\"1\"\u003e\u003csmall data-mce-fragment=\"1\"\u003e\u003csup data-mce-fragment=\"1\"\u003e3\u003c\/sup\u003e\u003cspan data-mce-fragment=\"1\"\u003e \u003c\/span\u003eDisplay ports are on by default for the RTX 4000 Ada 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