According to the International Data Corporation (IDC) predictions, Artificial Intelligence (AI) and Machine Learning (ML) expenditures will increase to 57.6 billion United States Dollar (USD) by 2021 as compared to 12 billion USD in 2017.
ML is based on data, and with the increasing amount of data, the ML model can adjust bias and variances to improve itself. Thus, ML organisations are achieving consistency and trust in cloud computing. When datasets are larger, cloud computing is preferred with GPUs (Graphics Processing Units).
GPU for ML:
GPUs accelerate the graphics processing and thus speed up ML computational processes. As an essential part of AI infrastructure, GPUs are specifically developed with attention to their applicability for ML. GPUs provide parallelism in computational processing, but according to their application, different GPUs are available such as data centre GPUs, consumer-grade GPUs, and GPU servers.
GPU uses Single instruction multiple data (SIMD), which enables various and simultaneous computations. It is a fundamental requirement of ML to carry out analyses on a vast dataset quickly. GPU allows accumulating many cores without sacrificing power or efficiency and uses fewer resources.
Choosing GPU for ML:
NVIDIA and AMD are the two prominent GPU market providers, in which NVIDIA occupies 76% of the GPU market share, and AMD occupies the remaining 14%. NVIDIA GPUs have
- A wider variety of Graphics Cards
- A massive range of stake of high-end Graphics Cards
- Faster memory bandwidths
As per the application and among available GPUs in the market, several factors should be considered while selecting the right GPU for you, such as
- Memory Bandwidth: GPUs include separate video RAM (VRAM) that enables retaining bandwidth for larger datasets. GPUs are optimised particularly for memory bandwidth, but they might be limited when a vast amount of memory is involved.
- Size of Dataset: With parallelism, GPU easily scales up CPUs and enables faster massive dataset processes, thus larger the dataset, most GPUs’ benefits.
One may think other factors such as clock speed and a CUDA core are also essential, but nothing is as important as the memory size.
How to Choose a GPU for ML?
GPUs should be durable, scalable, easy to use for large ML applications, integrated and clustered. One should choose the best GPU based on the following factors:
- GPUs’ interconnecting ability: Interconnecting GPUs are scalable and can be used for distributed training strategies and multi-GPU. Thus one should consider which GPU units can be interconnected while choosing it for ML.
- Software Support: NVIDIA GPUs best support ML libraries as the kit has in-built GPU-accelerated libraries, tools for debugging & optimisation, and C & C++ compiler and runtime that removes the need for custom integration building.
- Licensing: There are certain limitations on CUDA software usage per the Licensing update 2018 that should be considered while choosing GPUs.
- Cost: Though it seems less important at first glance, the optimised GPU with affordable cost is equally important while choosing GPU.
NVIDIA provides competent, powerful, and lucrative GPUs for ML such as NVIDIA Tesla and NVIDIA DGX.
NVIDIA Tesla: It is helpful for large-scale ML projects and data centres. Among the popular series, NVIDIA Tesla V100 is used for high-performance computing and ML.
NVIDIA DGX: It is used for enterprise-level ML applications as it is optimised for multiple node scalability. DGX series has complete integration capabilities with ML libraries as well as NVIDIA solutions.
For high-level ML applications, optimum performance and high memory are achievable on windows cloud with GPU technology. E2E Cloud offers cost-effective and best suitable GPU solutions catering to different customer requirements.
E2E Cloud provides NVIDIA GPUs on its cloud models with superior performance, scalability, trustworthiness, affordability, and advanced privacy features. E2E Cloud NVIDIA GPUs have power-packed performance. It improves the system’s abilities inexpensively with the latest GPU features like NVIDIA Tensor Core 100, V100, and RTX.
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