EDGE AI vs CLOUD AI
Today, when most people think of AI applications, they think of an application that is powered by the cloud. Cloud AI refers to processing that takes place away from the user’s device, typically in a far-off data centre. Software developers use cloud resources to train their models as this is typically a very resource-heavy process. But cloud AI also refers to the process of using a pre-trained model to calculate and return an answer to a user based on their input (this process is known as inference).
Edge AI on the other hand focuses on inference and only takes place on the device where the prompt is delivered or the sensor collects the data. To date, Edge AI experiences have been handicapped by insufficiently powerful hardware, and insufficiently efficient software. In recent years, however, there has been a wave of innovation on both sides and device markets are about to witness a surge in Edge AI applications.
Let’s take a look at the key differences between cloud AI and Edge AI, covering applications, pros and cons, hardware, and software considerations.
APPLICATIONS
EDGE AI
Vehicle Autonomy: Edge AI allows self-driving cars to interpret data from their sensors and make real-time decisions for vehicle movement.
Robotic Devices: Robots use Edge AI to understand and move around their environments and to communicate with the people around them.
Smart Homes: Smart speakers are loaded with advanced AI algorithms so that they can detect voices and interpret meaning. Other devices in the home can be used to optimise resources.
Wearables: Even small devices like smartwatches use AI to interpret sensor data into consumer-friendly information – like heartbeats per second.
CLOUD AI
Medical Imaging: Advanced computer vision techniques are helping doctors detect anomalies in diagnostic images faster and more accurately.
Content Creation: The latest generation of natural language processing models and image creation software has transformed the face of content creation, giving everyone the power to be a great wordsmith or designer.
Cybersecurity: AI is helping to protect businesses from IT threats by analysing the flow of internet traffic and detecting anomalies.
AI Assistants: Increasingly common in businesses, AI assistants can create accurate minutes from meetings, polish an email, and upskill employees in a short space of time.
BENEFITS
EDGE AI
Resilience: Because all data processing takes place on the device, Edge AI operates even without a stable connection to the internet.
Low Latency: Transmitting data to and from a central cloud server takes time. Edge AI can avoid this latency by processing data on the device.
Privacy: Transmitting data into third-party cloud services introduces an element of risk that for some types of information (financial, healthcare) are unacceptable.
Efficiency: Data transmission costs power, and while cloud AI models are more capable than most Edge AI models, they also consume a lot more energy.
CLOUD AI
Performance: Cloud AI has more computing resources available to accelerate applications and uses algorithms that prioritise speed and accuracy rather than efficiency.
Accessibility: Cloud services are available to anyone with an internet connection, whereas Edge AI does require a device with the right hardware.
WEAKNESSES
EDGE AI
Performance: Edge AI devices have less computing power, and the models need to be smaller than cloud equivalents, which can impact experience speed and quality.
CLOUD AI
Power consumption: Cloud AI requires a lot of energy. In USA, 2023 data centres are said to have consumed over 4% of US electricity. Simply doing a couple of queries on a large language model can consume the equivalent energy of an entire smartphone charge.
Cost: With pricing starting at about $8/day for access to a single entry-level GPU, operating costs can start to add up for businesses using cloud services for their AI applications.
HARDWARE
EDGE AI
Heterogeneous: Edge AI typically runs on an SoC that hosts a diverse range of accelerators (CPUs, GPUs, NPUs). AI laptops in 2025 are delivering about 75 TOPS from CPU, GPU, and NPU.
Diverse: The edge device ecosystem is incredibly diverse, and software developers must contend with different form factors, processors, and architectures.
CLOUD AI
Homogeneous: Historically cloud infrastructure has been CPU-based, but GPU-accelerated services are now growing in popularity to support AI and graphics applications. Customers can choose a platform and spread their application on multiple (identical) instances).
Few Vendors: You can count on one hand the number of companies supplying hardware into cloud systems.
SOFTWARE
EDGE AI
Small and Efficient: Edge AI models tend to be smaller than 10Bn parameters and optimised with techniques like sparsity and quantisation to deliver accurate results with lower power consumption.
CLOUD AI
Large and Accurate: Cloud AI models have started breaking the trillion-parameter mark and algorithmic innovations like self-attention mechanisms massively improving accuracy.
Here at Imagination, we develop the processors that deliver AI across the Edge. Find out more about our latest generation of Edge AI technology.
