Historically, the training phase of the deep learning pipeline has been the most time-consuming. Not only is this a time-consuming process, but it is also an expensive one. The most significant component of a deep learning pipeline is the human component — data scientists frequently wait hours or even days for training to complete, reducing their productivity and time to market for new models.
You may drastically shorten training time by utilizing deep learning GPUs, which enable parallel AI computing operations. When evaluating GPUs, examining their interconnect ability, the available supporting software, licensing, data parallelism, GPU memory usage, and performance are necessary.
Importance of GPUs in Deep Learning
The training step is the most time-consuming and resource-intensive phase of the majority of deep learning implementations. This phase can be completed in a fair amount of time for models with fewer parameters, but as the number of parameters increases, the training time increases proportionately. This has a dual cost: it keeps your resources busy longer and leaves your team waiting, squandering critical time.
GPUs can help you save money by enabling you to run models with a large number of parameters rapidly and efficiently. This is because GPUs enable parallelization of training workloads by dividing them over clusters of processors and concurrently conducting computational operations.
Additionally, GPUs are tailored for certain tasks, completing computations faster than non-specialized hardware. These processors enable you to complete identical jobs more quickly and free up your CPUs for other work. This removes bottlenecks caused by computational constraints.
How to select the Best GPU for Deep Learning?
Selecting the GPUs for your implementation has significant budget and performance implications. You need to select GPUs that can support your project in the long run and have the ability to scale through integration and clustering. For large-scale projects, this means selecting production-grade or data center GPUs.
Factors to Consider
These factors affect the scalability and ease of use of the GPUs you choose.
Ability to interconnect GPUs
When choosing a GPU, you need to consider which units can be interconnected. Interconnecting GPUs is directly tied to the scalability of your implementation and the ability to use multi-GPU and distributed training strategies.
Typically, consumer GPUs do not support interconnection (NVlink for GPU interconnects within a server and Infiniband/RoCE for linking GPUs across servers), and NVIDIA has removed interconnections on GPUs below RTX 2080.
NVIDIA GPUs are the best supported in machine learning libraries and integration with common frameworks, such as PyTorch or TensorFlow. The NVIDIA CUDA toolkit includes GPU-accelerated libraries, a C and C++ compiler and runtime, and optimization and debugging tools. It enables you to get started right away without worrying about building custom integrations.
Additionally, examine NVIDIA’s advice on the use of specific processors in data centers. As a result of a licensing update in 2018, there may be limits on the use of CUDA software in a data center using consumer GPUs. This may necessitate a switch to production-grade GPUs.
Factors Affecting GPU Utilization
Based on our expertise in optimizing large-scale deep learning workloads for enterprises, the following are the three critical things to consider when scaling your algorithm over several GPUs.
Consider the amount of data that your algorithms must process. If datasets will be huge, invest in GPUs capable of efficiently executing multi-GPU training. To enable efficient distributed training on very big datasets, ensure that servers can communicate extremely quickly with one another and with storage components, employing technologies such as Infiniband/RoCE.
Will you be modeling big amounts of data? For example, models processing medical images or lengthy films require extremely large training sets, necessitating the purchase of GPUs with sufficient memory. In comparison, tabular data, such as text inputs for NLP models, is often small, allowing for the use of less GPU RAM.
Consider the GPU’s performance if you intend to utilize it for debugging and development. In this instance, the most powerful GPUs are unnecessary. To tune models over extended runs, you’ll need powerful GPUs to expedite the training process and prevent having to wait hours or even days for models to run.
Best Deep Learning GPUs for Data Centers
The following GPUs are recommended for usage in large-scale artificial intelligence projects.
A100 by NVIDIA
The A100 is a multi-instance GPU (MIG) with Tensor Cores. It was created for machine learning, data analytics, and high-performance computing.
The A100 is designed to grow to thousands of units and partition into seven GPU instances to handle any type of demand. Each A100 processor delivers up to 624 teraflops of performance, 40 GB of memory, 1,555 GB of memory bandwidth, and interconnect bandwidth of 600 GB/s.
Tesla V100 NVIDIA
The NVIDIA Tesla V100 GPU features Tensor Cores and is optimized for machine learning, deep learning, and high-performance computing (HPC). It is driven by NVIDIA Volta technology, enabling tensor cores optimized for expediting typical tensor operations in deep learning. Each Tesla V100 has a performance of 149 teraflops, up to 32GB of memory, and a 4,096-bit memory bus.
Tesla P100 by NVIDIA
The Tesla P100 is a graphics processing unit (GPU) based on the NVIDIA Pascal architecture optimized for machine learning and high-performance computing (HPC). Each P100 has the performance of up to 21 teraflops, 16GB of memory, and a 4,096-bit memory bus.
Tesla K80 by NVIDIA
The Tesla K80 is a graphics processing unit (GPU) based on NVIDIA’s Kepler architecture optimized for scientific computing and data analytics. It comes equipped with 4,992 NVIDIA CUDA cores and NVIDIA GPU BoostTM technology. Each K80 has a maximum performance of 8.73 teraflops, 24GB of GDDR5 memory, and 480GB of memory bandwidth.
Google’s tensor processing units are slightly different (TPUs). TPUs are application-specific integrated circuits (ASICs) for deep learning that can be implemented on a chip or the cloud. These units are optimized for usage with TensorFlow and are currently only accessible on the Google Cloud Platform.
Each TPU is capable of 420 teraflops of performance and 128 GB of high-speed memory (HBM). Additionally, pod versions with over 100 petaflops of performance, 32TB HBM, and a 2D toroidal mesh network are available.
Utilization of Consumer-Grade GPUs for Deep Learning
While consumer GPUs are insufficient for large-scale deep learning projects, they can serve as a decent entry point into the field. Consumer GPUs can also augment less sophisticated tasks like model planning and low-level testing at a lower cost. However, if your business grows, you’ll want to investigate high-end deep learning systems like NVIDIA’s DGX series and data center-grade GPUs (learn more in the following sections).
When it comes to Word RNNs, the Titan V has been demonstrated to deliver performance comparable to datacenter-grade GPUs. Furthermore, its CNN performance is marginally inferior to higher-grade choices, and the RTX 2080 Ti and Titan RTX aren’t far behind.
Titan V by NVIDIA
The Titan V is a GPU for PCs that was created with scientists and researchers in mind. It features Tensor Cores and is based on NVIDIA’s Volta technology. Standard and CEO Editions of the Titan V are available.
The Standard model comes with 12GB of RAM, 110 teraflops of performance, a 4.5MB L2 cache, and a 3,072-bit memory bus. The CEO edition comes with 32GB of memory and 125 teraflops of performance and a 6MB cache and a 4,096-bit memory bus. The 8-Hi HBM2 memory stacks used in the 32GB Tesla units are also employed in the latter edition.
NVIDIA Titan RTX
The Titan RTX is a PC GPU geared for creative and machine learning applications and is based on NVIDIA’s Turing GPU architecture. Tensor Core and RT Core technologies enable Ray tracing and accelerated AI.
Each Titan RTX has 130 teraflops, 24 gigabytes of GDDR6 memory, a 6-megabyte cache, and 11 GigaRays per second. 72 Turing RT Cores and 576 multi-precision Turing Tensor Cores are responsible for this.
NVIDIA GeForce RTX 2080 Ti
The GeForce RTX 2080 Ti is an enthusiast-oriented PC GPU. It uses the TU102 graphics processor as its foundation. Each GeForce RTX 2080 Ti has 11GB of memory, a 352-bit memory bus, a 6-MB cache, and performance of around 120 teraflops.
NVIDIA DGX For Deep Learning
NVIDIA DGX systems are full-stack machine learning solutions. These systems are built on a software stack that is geared for artificial intelligence, multi-node scalability, and enterprise-grade support.
The DGX stack can be deployed in containers or on bare metal. This technology is plug-and-play, and it is fully integrated with NVIDIA deep learning libraries and software solutions. DGX is a server-class workstation, server, or pod solution. The server options are discussed more below.
The DGX-1 is a GPU server that runs the Ubuntu Linux Host operating system. It works with Red Hat solutions and comes with the DIGITS deep learning training application, the NVIDIA Deep Learning SDK, the CUDA toolkit, and the Docker Engine Utility for NVIDIA GPU.
- For deep learning framework coordination, boot, and storage management, two Intel Xeon CPUs are used.
- Up to 8 Tesla V100 Tensor Cores GPUs with 32GB memory and 300Gb/s bandwidth NVLink interconnects at 800GB/s with low latency.
- A single 480GB boot OS SSD and four 1.92 TB SAS SSDs configured as a RAID 0 striped volume (7.6 TB total).
The DGX-2 is the DGX-1’s step-up model. For higher parallelism and scalability, it is built on the NVSwitch networking fabric.
Each DGX-2 includes:
- Performance of two petaflops
- 2 x 960GB NVME SSDs for OS storage, with 30TB of SSD storage
- 16 Tesla V100 Tensor Core GPUs with a total memory capacity of 32GB
- 12 NVSwitches for a bisection bandwidth of 2.4TB/s
- Low-latency, bidirectional bandwidth of 1.6TB/s
- 1.5TB of total system memory
- For deep learning framework coordination, boot, and storage, two Xeon Platinum CPUs are used.
- Two Ethernet cards with a lot of I/O
The DGX A100 is intended to be a general-purpose system for machine learning tasks such as analytics, training, and inference, and it is entirely CUDA-X optimized. The NVIDIA DGX A100, together with other A100 units, may be stacked to form massive AI clusters, such as the NVIDIA DGX SuperPOD.
Each DGX A100 includes:
- Performance of five petaflops
- Eight A100 Tensor Core GPUs with a total memory capacity of 40GB
- Six NVSwitches provide 4.8TB of bidirectional bandwidth.
- Nine Mellanox Connectx-6 network ports with a bidirectional capacity of 450GB/s
- Two AMD 64-core CPUs are used for deep learning framework coordination, boot, and storage.
- 1 Terabyte of system memory
2x 1.92TB M.2 NVME drives for operating system storage and 15TB SSD storage