10 Best GPU for Deep Learning 2022 – Do Not Buy Before Reading This!

Best GPU for Deep Learning 2022 – Are you searching for new products? than you are at the correct page. As we have done our research for you guys. We have also written buyer’s guide for you.


Best GPU for Deep Learning

Best GPU for Deep Learning 2022


#01 – Toybrick RK3399Pro AI Development Kit for Artificial Intelligence Acceleration Deep Learning, Support TensorFlow Caffe up to 3.0TOPs, Android and Linux

Toybrick RK3399Pro AI Development Kit for Artificial Intelligence Acceleration Deep Learning, Support TensorFlow Caffe up to 3.0TOPs, Android and Linux
  • [High-performance AI Processor RK3399Pro] TB-RK3399ProD adopts big.LITTLE core processor architecture of ARM dual-core Cortex-A72 and quad-core Cortex-A53 at a high frequency as 1.8GHz, integrated Mali-T860 MP4 quad-core graphics processor with powerful general-purpose computing performance
  • [Support Multiple AI Framework] Compatible with multiple AI frameworks, supports TensorFlow Lite/Android NN API, AI software tools support import, mapping, and optimization of Caffe / TensorFlow models, allowing developers to use AI technology easily
  • [Rich Extension Interfaces] Rich in interfaces such as I2C, SPI, UART, ADC, PWM, 40 pin GPIO, PCIex4(Support High Speed WiFi and SSD Module),USB3.0 OTGx1, I2S (supporting 8 digital microphone array inputs) and Support 2.4G & 5G WiFi,support 802.11b/g/n potocol Support Bluetooth4.2;Support Debian, Fedora-Android Dual OS
  • [Wide Application] Industrial automation, UAV, image detection, face recognition, edge computing gateway, cluster server, Intelligent Quotient display, automatic driving, and medicine

#02 – Deep Learning DevBox Mini Edition – Intel Core Extreme X299 for CUDA Development, AI Inference (Intel Core i9-9920X 12-Core Processor, 1 x Tesla T4)

Deep Learning DevBox Mini Edition - Intel Core Extreme X299 for CUDA Development, AI Inference (Intel Core i9-9920X 12-Core Processor, 1 x Tesla T4)
  • Fully Configured with Ubuntu and Widely Used Deep Learning Frameworks
  • Includes Intel Core Extreme Processor, Nvidia GPGPU, 64GB Memory, 1TB NVMe M.2 Boot Drive, and 4TB 3.5" Enterprise Storage Drive
  • Supports up to 2 x GPGPU; Supports up to 64GB Memory; Supports up to 6 x drives
  • Ubuntu 18.04 LTS, NVIDIA CUDA, TensorFlow, Caffe2, PyTorch, Mxnet, DL4J, Docker, Keras
  • Case Dimensions: 397mm x 260mm x 320mm, 15.63" x 10.24" x 12.60"

#03 – 2U, 8 x NVIDIA GeForce RTX 2080 TI 11GB GPU Gigabyte G291-280 Deep Machine Learning AI Rendering HPC Graphics Rack Server Computer System for High Density Enterprise (Dual Xeon 3104 CPU, 96GB ECC RAM)

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#04 – Deep Learning DevBox – Intel Core Extreme X299 for CUDA, AI Inference, Machine Learning (i7-9800X 8-Core Processor, 2 x Tesla T4)

Deep Learning DevBox - Intel Core Extreme X299 for CUDA, AI Inference, Machine Learning (i7-9800X 8-Core Processor, 2 x Tesla T4)
  • Fully Configured with Ubuntu and Widely Used Deep Learning Frameworks
  • Includes Intel Core X-Series Processor, Nvidia GPGPU, 64GB Memory, 1TB NVMe M.2 Boot Drive, and 4TB 3.5" Enterprise HDD Storage Drive
  • Supports up to four GPGPU, Supports up to 128GB Memory, Supports up to 8 x drives
  • Configured with Ubuntu 18.04 LTS, NVIDIA CUDA, TensorFlow, Caffe2, PyTorch, Mxnet, DL4J, Docker, Keras
  • Case Dimensions: 415 x 332 x 458m, 16.34" x 13.07" x 18.03" in inches

#05 – youyeetoo NanoPi M4V2 RK3399 SoC Based ARM Board Compatible with Ports and interfaces for RPi B3+ Supports Android 8.1 and Ubuntu Desktop 18.04 for deep Learning,Game Machines, Blockchain

youyeetoo NanoPi M4V2 RK3399 SoC Based ARM Board Compatible with Ports and interfaces for RPi B3+ Supports Android 8.1 and Ubuntu Desktop 18.04 for deep Learning,Game Machines, Blockchain
  • The NanoPi M4 is a RK3399 SoC based ARM board. It has the same form of factor as the RPi B3+ and has ports and interfaces compatible with RPi B3+ too. On a 85 X 56 mm compact board there are rich hardware resources.
  • The NanoPi M4 has an onboard 2.4G & 5G dual-band WiFi and Bluetooth module, four USB3.0 Type A host ports, one Gbps Ethernet port, one HDMI 2.0 Type A port, one 3.5mm audio jack and one Type-C port. In addition it has a RPi compatible 40-pin connector, dual MIPI-CSI camera interface, PClex2, USB 2.0, eMMC socket, RTC port and etc.
  • The NanoPi M4 has 2GB DDR3 RAM. Itcan be booted from either a TF card or an external eMMC module. The NanoPi M4 supports Ubuntu Desktop 18.04(64-bit), Lubuntu 16.04(32-bit), Ubuntu Core 18.04(64-bit), Android 8.1 and Lubuntu Desktopwith GPU and VPU acceleration.
  • With these rich resources and powerful performance it can be widely used in applications of machine learning, Al, deep learning, robots, industrial control, industrial cameras, advertisement machines, game machines, blockchain and etc.

#06 – Hydra VIII Modular 6.5U Case for 10 GPU Mining Rendering AI Servers, Triple PSU Ready

Hydra VIII Modular 6.5U Case for 10 GPU Mining Rendering AI Servers, Triple PSU Ready
  • Pre-manufactured assemblies minimize the number of tiny parts you need to put together; Measures 290 x 570 x 500 mm when fully assembled.
  • Fits 10 dual-slot aftermarket cards with bigger PCB/cooler up to 330 mm length; Switchable to 8 triple-slot patterns for increased space and better airflow.
  • Uses 3x power supplies for improved power distribution; Not compatible with 19 inch rack enclosure due to the extra width.
  • Extra tall 6.5U chassis elevates your cards above the MB and PSU, and keeps the cables out of critical airflow paths.
  • Supports ATX/Micro-ATX motherboard, 3 PSU, 8 or 10 GPU, and 8 x 120 mm fans.

#07 – NVIDIA Jetson AGX Xavier Developer Kit (32GB)

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NVIDIA Jetson AGX Xavier Developer Kit (32GB)
  • Newly updated version with an additional 16GB of memory for a total of 32GB of 256-bit wide LPDDR4X memory.
  • NVIDIA Jetson Xavier is an AI computer for Autonomous Machines with the performance of a GPU workstation in under 30W
  • The Jetson Xavier Developer Kit with Jetson Xavier module and reference carrier board is the fastest way to start prototyping with robots, drones and other autonomous machines
  • Visit the NVIDIA Jetson developer site for the latest software, documentation, sample applications, and developer community information
  • System Ram Type: Ddr Dram

#08 – FriendlyElec NanoPC-T4 Open Source RK3399 ARM Development Board LPDDR3 RAM 4GB Gbps Ethernet,Support Android and Ubuntu, AI and deep Learning

NanoPC-T4 RK3399 ARM Dual-Display Mini PC LPDDR3 RAM 4GB Gbps Ethernet,Support Android 8.1 and Lubuntu 16.04, AI Project
  • Dual Camera Interface & Dual 4K output: Supports simultaneous input of dual camera data annd dual display output.,Perfect Platform for VR, AI,Machine Learning anddeep vision Applications. FriendlyElec's Android 8.1 suppots display rotation. You can rotate display by 0*/90*/180*/270* degrees
  • M.2 NVME PCle x4: The NanoPC-T4's M.2 M-Key PClex4 interface has powerful expansion capabilities. It supports SSD storage expansion and expansion interfaces including STAT, USB3.0/3.1, 1G/10Gbps Ethernet, high speed WiFi and etc
  • SuperSpeed USB Interface: USB 3.0 Type-A,10 times faster than USB 2.0,Up to 5.0Gbps; USB Type-C is a dual-role port and it supports VESA DisplayPort Alt Mode for USB Type C standard
  • Docker on Ubuntu,Open Source on Github.: Docker is a platform for developers and sysadmins to develop, deploy, and run applications with containers.And Docker is now supported in all three FriendlyElec's systems for RK3399: FriendlyDesktop, FriendlyCore and Lubuntu Desktop

#09 – NVIDIA Jetson Nano Developer Kit (945-13450-0000-100)

NVIDIA Jetson Nano Developer Kit (945-13450-0000-100)
  • The NVIDIA Jetson Nano Developer Kit delivers the compute performance to run modern AI workloads at unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing.
  • The developer kit can be powered by micro-USB and comes with extensive I/Os, ranging from GPIO to CSI. This makes it simple for developers to connect a diverse set of new sensors to enable a variety of AI applications. And it is incredibly power-efficient, consuming as little as 5 watts.
  • Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA, cuDNN, and TensorRT software libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more. The software is even available using an easy-to-flash SD card image, making it fast and easy to get started.
  • The same JetPack SDK is used across the entire NVIDIA Jetson family of products and is fully compatible with NVIDIA’s world-leading AI platform for training and deploying AI software. This proven software stack reduces complexity and overall effort for developers.

#10 – NVIDIA Tesla M40 24GB GDDR5 PCI-E 3.0X16 GPU Accelerator Graphics Card W/ Cable


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