Graphic processing units or GPUs are specialised processors with dedicated memory to perform floating-point operations. GPU is very useful for deep learning tasks as it helps in reducing the training time by simply running all the operations at the same time instead of one after another. GPUs can deal with complex operations efficiently and help with deep learning functions such as matrix manipulation, only computing prerequisites, and computing power.
Some of the common aspects to look at while choosing a GPU for your project include GPU RAM, cores and tensor cores. In this article, we list some of the GPUs best suited for deep learning projects.
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ZOTAC GeForce GTX 1070
ZOTAC GeForce GTX 1070 Mini Graphic Card is a miniature graphic card that packs many features. Inspired by NVIDIA’s Pascal architecture, the GPU provides high performance, improved memory bandwidth, and power efficiency with a newly-vamped high-performance Maxwell architecture. It runs on a GP104 chip, clocking as much as 1708 MHz, allowing the user to play the games on 4K at 60fps. The graphic card requires a single eight-pin PCI power connector for its three display port 1.4 connectors and 2 HDMI 2.0b ports.
ZOTAC GeForce GTX 1070 has two fans and a metal backplate that allows the card to handle VR titles because it is mighty efficient. Its innovative graphic feature is combined with the tech allowing it to redefine the computer as the platform for AAA games.
NVIDIA GeForce RTX 2060
The GeForce RTX 2060 is powered by NVIDIA’s Turing architecture that ensures higher performance and the power of real-time ray tracing. RTX 2060 provides up to six times the performance compared to its predecessors. The GPU is a suitable choice for graphically intensive PC games with its dual feature allowing the user to game and stream it simultaneously with superior quality. The system is powered by a boost clock of 1680 MHz, a frame buffer worth 6GB GDDR6 and 14 Gbps of memory speed.
NVIDIA Tesla K80
This GPU can save data centre energy while boosting throughput in real-world applications. This feature means improved performance for the GPU. The core consists of a dual-GPU design, 24GB of GDDR5 memory, 480 GB/s aggregate memory bandwidth, ECC protection for increased reliability and server-optimisation.
This GPU combines two graphics processors to increase performance. NVIDIA Tesla K80 is a dual-slot card drawing power from a 1×8-pin power connector.
The NVIDIA GeForce GTX 1080
Powered by NVIDIA’s famous Pascal architecture, NVIDIA GeForce GTX 1080 has improved performance and power efficiency. NVIDIA claims that Pascal can deliver “thrice the performance of previous-generation graphics cards, along with its new gaming technologies and breakthrough VR experiences.” Its GTX’s unique features include a premium material and a vapour chamber cooling technology.
The GPU supports DirectX 12 and features a large chip with a die area of 7,200 million transistors. It also offers the users updates for a new architecture framework, double the frame buffer RAM, 30 percent faster memory speed, and more juice out of the boost clock.
The NVIDIA GeForce RTX 2080
Powered by NVIDIA’s next-generation Turing architecture, the company claims a “6X the performance from previous-generation graphics cards.”
The GPU boasts a dual-axle 13-blade fan coupled with a vapour chamber for cooler and quieter performance. NVIDIA has paired 8 GB GDDR6 memory with this model, connected using a 256-bit memory interface. The GPU operates at the frequency of 1515 MHz that can be further boosted up to 1710 MHz.
The NVIDIA GeForce RTX 3060
NVIDIA GeForce RTX 3060 is based on NVIDIA’s Ampere architecture- the second-generation RTX framework. This system offers “Ray Tracing Cores and Tensor Cores, new streaming multiprocessors, and high-speed G6 memory.” In addition, the GPU promotes NVIDIA’s Deep Learning Super Sampling- the company’s AI that boosts frame rates with superior image quality using a Tensor Core AI processing framework.
The system comprises 152 tensor cores and 38 ray tracing acceleration cores that increase the speed of machine learning applications. The dimensions are card- 242 mm in length, 112 mm in width with a dual-slot cooling solution.
The NVIDIA Titan RTX
The NVIDIA Titan RTX is a handy tool for researchers, developers and creators. This is because of its Turing architecture, 130 Tensor TFLOPs, 576 tensor cores, and 24GB of GDDR6 memory. In addition, the GPU is compatible with all popular deep learning frameworks and NVIDIA GPU Cloud.
The NVIDIA Titan RTX is a dual-slot card with a DirectX 12 Ultimate capability. This builds the support for its hardware-ray tracing and variable-rate shading.
ASUS ROG Strix Radeon RX 570
ASUS ROG Strix Radeon RX 570 has a higher core count, better clock boosting technology, and faster memory. The GPU additionally uses Navi 14 GPU, has a clock speed of 1737 MHz game clock and an 1845 MHz boost clock. Its 8 GB of GDDR6 memory sits on a 128-bit bus and runs at 1750 MHz with a bandwidth of 224 Gbps. The graphics card also allows for an enhanced gaming experience with its 130W total band power.
NVIDIA Tesla V100
The NVIDIA Tesla V100 is highly advanced with its Tensor core-based data centre GPUs. Based on NVIDIA’s Volta architecture, the GPU accelerates AI and deep learning performance by a large portion. For instance, a single V100 server is adept at providing the execution of hundreds of traditional CPUs. The system comprises 640 Tensor cores and has a 130 teraflops (TFLOPS) performance and a next-generation NVLink.
The NVIDIA A100 allows for AI and deep learning accelerators for enterprises. The GPU has high-performance computing (HPC), enhanced acceleration, and data analytics to encompass complex computer challenges. Its high power allows it to scale up to thousands of GPUs and divide the workload over multiple instances. The system consists of close to 624 teraflops performance for deep learning, Next-generation NVLink and 40 GB high performing GPU memory.