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Cloud GPU Market And AI: Trends And Analysis 2020

Cloud GPU Market And AI: Trends And Analysis 2020

AI is a top priority for CIOs

Cloud spending in India is estimated to grow at 30% p.a. to reach $7.1 Bn in 2022

Cloud GPU market is expected to see 30% growth to hit $5 Bn by 2024

GPUs are highly-effective for deep learning workloads. Manufacturers like NVIDIA have designed state of the art GPUs like Tesla V100 to enable AI engineers and data scientists to do more. GPUs are good at parallel-processing, and they dramatically bring down the time taken and consequently the total cost of developing AI/ML models.

According to a Gartner survey, AI adoption in organizations has tripled in the past year, and AI is a top priority for CIOs.

Major trends contributing to the uptick of cloud GPU market are:

Growth In Adoption Of Cloud

Startups, SMEs, and enterprises alike are embracing cloud computing and are moving their application workloads and storage systems to cloud. According to a report by NASSCOM, cloud spending in India is estimated to grow at 30% p.a. to reach USD 7.1 billion in 2022.

GPUs On The Cloud

Procuring and maintaining a large number of GPUs working in parallel for real-time decision making involves massive upfront costs, not justified by overall utilization spread over a month. One of the identity verification startups we spoke to uses about 150 or more GPU instances during the peak a couple of times a month but overall monthly utilization is less than 10% on average.

These type of GPU workloads utilized in real-time makeup for an ideal candidate for usage on the cloud rather than a fixed GPU utilization strategy in the form of a traditional dedicated server.

According to Global Market Insights, Cloud GPU market is expected to see 30% growth to hit $5 Bn by 2024.

Rise Of AI And Machine Learning

Machine Learning has generated a lot of interest in the Indian startup ecosystem and many startups have already been innovating in this space. According to a PwC report, 63% of Indian IT decision makers indicate that machine learning-based solutions are highly impactful. And, AI will be one of the top workloads that drives infrastructure decisions through 2023, according to Gartner.

SigTuple, a Bengaluru-based startup, uses deep learning to analyze medical data and generate reports, decreasing the time and human effort required to deliver timely patient-care. Another Bangalore based startup ArtiVatic uses machine learning to power insurance, finance & healthcare businesses with intelligent systems, solutions and processes.

Indian unicorns have been actively acquiring AI startups: Swiggy acquired — which specializes in object recognition, Flipkart acquired — which specializes in speech recognition, Oyo acquired AblePlus — which uses AI to improve hospitality management.

Major unicorns in the Indian startup ecosystem have acquired at least one AI company in the last two years. An indicator that AI and machine learning play a key role in sustaining competitive advantage and delivering the superior user experience.

Also, various technologies like text analytics, video analysis, speech and voice recognition that use machine learning are forecasted to grow in the next few years:

  • The global text analytics market was valued at USD 4.65 billion in 2018 and is expected to reach a value of USD 12.65 billion by 2024 at a CAGR of 17.35% over the forecast period (2019-2024), according to a report by Mordor Intelligence.
  • The global analytics market is expected to reach USD 14,443 million by 2015, according to a report by Allied Market Research.
  • The overall speech and voice recognition market is expected to reach USD 21.5 billion by 2024 from USD 7.5 billion in 2018, at a CAGR of 19.18%, according to a report by MarketsandMarkets.

In Conclusion

The demand for Cloud GPU instances in the compute infrastructure market is majorly driven by rapid adoption of cloud-first approach and the vital role of GPUs in the machine learning and deep learning space.

Machine learning has become a major driver in improving business processes. Developing machine learning capabilities within an organization is now considered essential. Organizations should start, if they haven’t yet, to experiment with machine learning and deep learning models to deliver better value from their IT spends.

Note: The views and opinions expressed are solely those of the author and does not necessarily reflect the views held by Inc42, its creators or employees. Inc42 is not responsible for the accuracy of any of the information supplied by guest bloggers.