Top 5 GPUs for deep learning & training

The working principle of the GPU:

Binary data is collected by translators to project it in a form of an image, unless otherwise the motherboard itself has a built-in graphics capability – in which case the translation occurs on the graphics card. With 3D images, an outlying wireframe is designed out of straight lines, which is then rasterized as well as filled in with texture, lightning, and color. For High speed animations, this cycle has to be completed about sixty times per second. GPUs are a vital component when it comes to handling computer graphics. 

Key features:

  •   Automatic colorization of black and white images 
  •   Automatic image captioning 
  •   Computerized machine translation 
  •   Automatically object classification and handwriting and story generations

The best GPUS for deep learning:

GTX 1080:

With an efficient performance, and low power requirement of 600-watt GTX 1080 excels in its own class. It is equipped with a 32-bit memory along with a 1.3, and 1.4 display port. The bandwidth with a high frequency of 1250 MHz oscillates around 320 GB/s. Accelerate your learning experience with GeForce GTX 1080, built on the finest NVIDIA pascal-architecture, delivering high grade performance, high speed computation, with potent power supply. With an inculcated vapor chamber heat is vented out, preventing premature failure. 

GTX 1080 TI:

GTX 1080 TI is a 10 series gaming GPU with a 11 Gbps memory speed, and a 484 GB/sec memory bandwidth is the most efficient one in its class. It brings on board a multi-monitor support, exceptional horsepower, massive 11 GB frame buffer, and next-generation 11 Gbps GDDR5X memory, and is considered a flagship to machine learning techniques. Included in its various benefits is the 360-degree viewable in-display screenshots that the NVIDIA ANSEL in VR lets you capture; the captured screenshots can further be adjusted with post-process filters. 


TITAN V is the most powerful GPU with a 1200 MHz base clock, and 850 MHz memory clock frequency – optimal for most devices. It enhances the overall capability of your graphics processing unit with its power connectors, and 6 pins. The total video memory of 12288 MB along with 6 graphics processing clusters makes it superior to its counterparts. This supercomputing GPU architecture, and Volta-based graphics card is a sustaining breakthrough in the visual graphics field.

GTX 980:

With the 2048 processing core, GTX 980 is manufactured to support deep learning, and machine learning techniques. It has the capacity to deliver 5 teraflops at the power rate of 165-watt. Most complex computing devices now deploy GTX 980 for high speed good quality graphics. It is said to take over the next-generation computing, with its built-in pioneering features addressing the most critical visual challenges. 


TITAN X comes with a multi projection support, and a memory speed of 11.4 GB. It is Driven by 3584 NVIDIA CUDA cores running with a fierce force of 1.5GHz, TITAN X packs 11 TFLOPS, thereby maximizing computational performance, and speed. With its energy efficient performance, and unparalleled graphics output it outclasses other GPUs in its league. NVIDIA VXGI together with NVIDIA G-SYNC display technology, let’s you experience a stable, faster, glitches-free processing, even on a 1080 display. 

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