Deep learning and machine learning have become increasingly vital for most organizations and firms. From automated driving systems to medical devices, everything leverages deep learning algorithms to perform its tasks. In traditional hardware architecture, deep learning models used to take a long time to achieve their goals. But with the advent of GPUs. The deep learning pipeline became efficient, productive, and less expensive. In this article, you will get to know the top five GPUs required for deep learning.
Why do you need GPUs for Deep Learning?
The most resourceful and intensive processing phase of a deep learning model is training the algorithm. Developers can accomplish this process in a reasonable time if they seek the help of a GPU to train the model. GPU allows running models with a large number of parameters. With the increase in the number of parameters during deep learning model training, the time also increases consecutively. Leveraging GPUs can help enhance performance significantly. The GPU systems distribute the task over its cluster of processors that helps in computing operations simultaneously.
Factors to Consider Before Buying a GPU for Deep Learning
It is pretty astounding to see how this little hardware has changed over the years and helps in training deep learning models. Picking your GPU to implement it in deep learning has significant performance implications with its budget. You have to choose the GPU in a way that can support your project eventually and is scalable by integration and clustering. If it is a large-scale project, you must go with the data center or production-grade types of GPUs. Other factors that affect GPU usage are:
- Data parallelism: It determines how much data a deep learning algorithm needs to process to perform multi-GPU training effectively.
- Performance in terms of programming: GPU performance is a critical factor for the development, accelerating training time, and debugging
- Memory usage: If your machine learning project or deep learning project has to deal with massive data inputs with semi-structured & unstructured data, your GPU needs more memory. Thus, it becomes a significant factor to consider before buying any GPU.
Top Five GPUs for Deep Learning
GPUs not only optimize the target tasks but also operate faster than non-specialized hardware. Since deep learning algorithms require diverse parameters, GPUs eliminate the computing bottleneck and enable multi-GPU or distributed training strategies. Let us now take a look at some of the best GPUs for deep learning.
- 1. NVIDIA GeForce RTX 2080 Ti: It is a new flagship graphics card of NVIDIA. It not only revolutionizes gaming realism but also utilizes Turing GPU architecture for training deep learning models through data parallelism. Due to its Turing GPU architecture, it gives 6X more performance boost than that of a previous-gen graphics card. It employs the TU102 graphics processor. Each of its RTX 2080 Ti renders 11 GB of next-generation, ultra-speed GDDR6 memory. It also has a 352-bit memory bus with approximately 120 teraflops of performance and a 6 MB cache. This GPU acts as a consumer GPU for general deep learning projects and comes at a suitable price of 1,199 USD.
- 2. NVIDIA Titan RTX: The Titan RTX is another top-notch PC GPU of NVIDIA powered by Turing GPU architecture. High graphics processing, machine learning, and Deep learning workloads leverage its Tensor Core and RT Core technologies for enabling accelerated AI processing and ray tracing. It comes with 130 teraflops and 576 multi-precision tensor cores. It also provides 24 GB GDDR6 memory, 11 GigaRays per second speed, and a 6 MB cache. Its Turing architecture includes 72 RT cores that provide real-time ray tracing and make deep learning training smooth. It comes at around 2,649.99 USD and is suitable for general-purpose deep learning projects.
- 3. NVIDIA Tesla K80: It is another NVIDIA GPU card based on the NVIDIA Kepler architecture. It drastically lowers the data center’s processing costs by producing outstanding performance with less yet more robust servers. Data scientists and deep learning professionals can accelerate experimental computing and heavy data analytics tasks using this. This GPU can boost real-world application throughput by 5 to 10x. Also, this GPU helps in rendering the customer responses with an acceleration of up to 50 percent than CPU-based data centers. It comprises 4,992 CUDA cores plus GPU Boost technology for the efficient performance of large-scale deep learning projects. Each K80 supports up to 8.73 teraflops (trillion floating-point operations per second), 480 GB bandwidth in memory, and 24 GB of GDDR5 memory space. This powerful data center GPU helps researchers in delivering scientific discoveries. This GPU comes at around 1,694.63 USD.
- 4. NVIDIA Tesla V100: It is another NVIDIA’sNVIDIA’s tensor-enabled GPU powered by Volta technology designed to perform machine learning, deep learning, data science, and high-end graphics editing. It comes with 16 & 32 GB configurations specialized for enhancing general tensor-based operations for deep learning models. It contains 149 teraflops of performance with a 4096-bit memory bus. Data scientists, deep learning professionals, researchers, and AI experts can leverage this GPU for large-scale breakthrough projects. This GPU costs 8,609 USD.
- 5. NVIDIA Tesla A100: The A100 GPU of NVIDIA powered by tensor cores provides unprecedented acceleration of deep learning and machine learning algorithm development. It combines multi-instance GPU (MIG) technology for high-performance computing (HPC) to deal with some of the world’s most challenging computing scenarios. It can tackle some of the most intricate AI, ML, and data analytics computations and scale up to thousands of units because it can partition itself into 7 GPU instances workloads of dynamic size. It supports 624 teraflops (trillion floating-point operations per second) performance, 1,555 GB of memory bandwidth, and 40 GB memory space. It incorporates a third-gen tensor core for precision for diverse workloads and supports 600 GB/s interconnects. This GPU is available at 16,312 USD.
GPUs with dedicated memory and processing power can reduce the training time and deal with complicated operations efficiently. It utilizes GPU RAM, tensors, and ray-tracing techniques to perform deep learning training with full computation power. What do you think, according to you, is the best among these?