Artificial Intelligence (AI) has become an integral part of our daily lives in recent years. However, it has been tied to running in huge, centralized cloud data centers. This year, “local AI”, also known as “on-device AI” or “Edge AI”, is gaining momentum. Local vector databases, efficient language models (so-called Small Language Models, SLMs), and AI algorithms are becoming smaller, more efficient, and less compute-heavy. As a result, they can now run on a wide variety of devices, locally.
What is Local AI (on-device AI, Edge AI)?
Local AI refers to running AI applications directly on a device, locally, instead of relying on (distant) cloud servers. Such an on-deivce AI works in real-time on commodity hardware (e.g. old PCs), consumer devices (e.g. smartphones, wearables), and other types of embedded devices (e.g. robots and point-of-sale (POS) systems used in shops and restaurants). An interest in local Artificial Intelligence is growing (see Figure 2).
Why use Local AI: Benefits
Local AI addresses many of the concerns and challenges of current cloud-based AI applications. The main reasons for the advancement of local AI are:
- Privacy / Data Security – Data stays on the device and under one’s control
- Accessibility – AI works independently from an internet connection
- Sustainability – AI consumes significantly less energy compared to cloud setups
On top, local AI reduces:
- latency, enabling real-time apps
- data transmission and cloud costs, enabling commodity business cases
In short: By leveraging the power of Edge Computing and on-device processing, local AI can unlock new possibilities for a wide range of applications, from consumer applications to industrial automation to healthcare.
Privacy: Keeping Data Secure
In a world where data privacy concerns are increasing, local AI offers a solution. Since data is processed directly on the device, sensitive information remains local, minimizing the risk of breaches or misuse of personal data. No need for data sharing, and data ownership is clear. This is the key to using AI responsibly in industries like healthcare, where sensitive data needs to be processed and used without being sent to external servers. For example, medical data analysis or diagnostic tools can run locally on a doctor’s device and be synchronized to other on-premise, local devices (like e.g. PCs, on-premise servers, specific medical equipment) as needed. This ensures that patient data never leaves the clinic, and data processing is compliant with strict privacy regulations like GDPR or HIPAA.
Accessibility: AI for Anyone, Anytime
One of the most significant advantages of local AI is its ability to function without an internet connection. This opens up a world of opportunities for users in remote locations or those with unreliable connectivity. Imagine having access to language translation, image recognition, or predictive text tools on your phone without needing to connect to the internet. Or a point-of-sale (POS) system in a retail store that operates seamlessly, even when there’s no internet. These AI-powered systems can still analyze customer buying habits, manage inventory, or suggest product recommendations offline, ensuring businesses don’t lose operational efficiency due to connectivity issues. Local AI makes this a reality. In combination with little hardware requirements, it makes AI accessible for anyone, anytime. Therefore, local AI is an integral ingredient in making AI more inclusive and to democratize AI.
Sustainability: Energy Efficiency
Cloud-based AI requires massive server farms that consume enormous amounts of energy. Despite strong efficiency improvements, in 2022, data centers globally consumed between 240 and 340 terawatt-hours (TWh) of electricity. To put this in perspective, data centers now use more electricity than entire countries like Argentina or Egypt. This growing energy demand places considerable pressure on global energy resources and contributes to around 1% of energy-related CO2 emissions. The rise of AI has amplified these trends. AI workloads alone could drive a 160% increase in data center energy demand by 2030, with some estimates suggesting that AI could consume 500% more energy in the UK than it does today. By that time, data centers may account for up to 8% of total energy consumption in the United States. In contrast, local AI presents a more sustainable alternative, e.g. by leveraging Small Language Models, which require less power to train and run. Since computations happen directly on the device, local AI significantly reduces the need for constant data transmission and large-scale server infrastructure. This not only lowers energy use but also helps decrease the overall carbon footprint. Additionally, integrating a local vector database can further enhance efficiency by minimizing reliance on power-hungry data centers, contributing to more energy-efficient and environmentally friendly technology solutions.
When to use local AI: Use case examples
Local AI enables an infinite number of new use cases. Thanks to advancements in AI models and vector databases, AI apps can be run cost-effectively on less capable hardware, e.g. commodity PCs, without the need for an internet connection and data sharing. This opens up the opportunity for offline AI, real-time AI, and private AI applications on a wide variety of devices. From smartphones and smartwatches to industrial equipment and even cars, local AI is becoming accessible to a broad range of users.
- Consumer Use Cases (B2C): Everyday apps like photo editors, voice assistants, and fitness trackers can integrate AI to offer faster and more personalized services (local RAG), or integrate generative AI capabilities.
- Business Use Cases (B2B): Retailers, manufacturers, and service providers can use local AI for data analysis, process automation, and real-time decision-making, even in offline environments. This improves efficiency and user experience without needing constant cloud connectivity.
Conclusion
Local AI is a powerful alternative to cloud-based solutions, making AI more accessible, private, and sustainable. With Small Language Models and on-device vector databases like ObjectBox, it is now possible to bring AI onto everyday devices. From the individual user who is looking for convenient, always-available tools to large businesses seeking to improve operations and create new services without relying on the cloud – local AI is transforming how we interact with technology everywhere.