The Latin America, Middle East and Africa Edge Artificial Intelligence Chips Market is expected to witness market growth of 36.3% CAGR during the forecast period (2024-2031).
The Brazil market dominated the LAMEA Edge Artificial Intelligence Chips Market by country in 2023, and is expected to continue to be a dominant market till 2031; thereby, achieving a market value of $2.87 billion by 2031. The Argentina market is experiencing a CAGR of 38.1% during 2024-2031. Additionally, the UAE market is expected to exhibit a CAGR of 34.9% during 2024-2031.
Edge AI chips significantly reduce latency by processing data locally rather than relying on cloud-based servers. This is crucial for applications requiring real-time responses, such as autonomous driving and industrial automation. The ability to make instant decisions based on real-time data enhances these systems' overall efficiency and reliability.
Data privacy and security concerns are driving the adoption of edge AI chips. By processing data locally, these chips minimize the risk of data breaches and ensure that sensitive information remains secure. This is especially important in sectors like healthcare and finance, where data privacy is paramount. While the initial investment in edge AI chips may be high, the long-term cost efficiency is a significant factor in their adoption. Businesses can save on bandwidth costs and improve operational efficiency by reducing the need for constant data transmission to cloud servers and lowering latency.
The burgeoning IoT industry in Saudi Arabia is expected to drive the demand for edge AI chips in multiple sectors, including smart cities, healthcare, transportation, and industrial automation. For instance, edge AI chips in smart cities can be used to manage traffic flows, monitor air quality, and optimize energy consumption. In healthcare, these chips enable real-time patient monitoring and diagnostics, enhancing the quality of care and operational efficiency. In transportation, edge AI chips facilitate the development of smart mobility solutions, such as autonomous vehicles and intelligent traffic management systems. The industrial sector can leverage edge AI chips for predictive maintenance, process optimization, and enhancing the safety and efficiency of operations. In conclusion, the UAE and Saudi Arabia are poised to become significant markets for edge AI chips due to their ambitious smart city initiatives and rapid expansion of the IoT industry.
The Brazil market dominated the LAMEA Edge Artificial Intelligence Chips Market by country in 2023, and is expected to continue to be a dominant market till 2031; thereby, achieving a market value of $2.87 billion by 2031. The Argentina market is experiencing a CAGR of 38.1% during 2024-2031. Additionally, the UAE market is expected to exhibit a CAGR of 34.9% during 2024-2031.
Edge AI chips significantly reduce latency by processing data locally rather than relying on cloud-based servers. This is crucial for applications requiring real-time responses, such as autonomous driving and industrial automation. The ability to make instant decisions based on real-time data enhances these systems' overall efficiency and reliability.
Data privacy and security concerns are driving the adoption of edge AI chips. By processing data locally, these chips minimize the risk of data breaches and ensure that sensitive information remains secure. This is especially important in sectors like healthcare and finance, where data privacy is paramount. While the initial investment in edge AI chips may be high, the long-term cost efficiency is a significant factor in their adoption. Businesses can save on bandwidth costs and improve operational efficiency by reducing the need for constant data transmission to cloud servers and lowering latency.
The burgeoning IoT industry in Saudi Arabia is expected to drive the demand for edge AI chips in multiple sectors, including smart cities, healthcare, transportation, and industrial automation. For instance, edge AI chips in smart cities can be used to manage traffic flows, monitor air quality, and optimize energy consumption. In healthcare, these chips enable real-time patient monitoring and diagnostics, enhancing the quality of care and operational efficiency. In transportation, edge AI chips facilitate the development of smart mobility solutions, such as autonomous vehicles and intelligent traffic management systems. The industrial sector can leverage edge AI chips for predictive maintenance, process optimization, and enhancing the safety and efficiency of operations. In conclusion, the UAE and Saudi Arabia are poised to become significant markets for edge AI chips due to their ambitious smart city initiatives and rapid expansion of the IoT industry.
List of Key Companies Profiled
- Advanced Micro Devices Inc.
- Samsung Electronics Co., Ltd. (Samsung Group)
- NXP Semiconductors N.V.
- Qualcomm Incorporated (Qualcomm Technologies, Inc.)
- NVIDIA Corporation
- Intel Corporation
- Infineon Technologies AG
- IBM Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Apple, Inc.
Market Report Segmentation
By Function
- Inference
- Training
By Chipset
- CPU
- ASIC
- GPU
- Other Chipset
By Device
- Consumer Devices
- Enterprise Devices
By Country
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. LAMEA Edge Artificial Intelligence Chips Market by Function
Chapter 6. LAMEA Edge Artificial Intelligence Chips Market by Chipset
Chapter 7. LAMEA Edge Artificial Intelligence Chips Market by Device
Chapter 8. LAMEA Edge Artificial Intelligence Chips Market by Country
Chapter 9. Company Profiles
Companies Mentioned
- Advanced Micro Devices Inc.
- Samsung Electronics Co., Ltd. (Samsung Group)
- NXP Semiconductors N.V.
- Qualcomm Incorporated (Qualcomm Technologies, Inc.)
- NVIDIA Corporation
- Intel Corporation
- Infineon Technologies AG
- IBM Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Apple, Inc.
Methodology
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