Edge AI refers to decentralized artificial intelligence systems that process data locally on in-store devices, e.g. POS terminals, smart shelves, Raspberry Pis, mobile phones, or cameras, rather than relying on distant cloud servers. This architecture works independently from distant cloud servers or internet connectivity, and therefore offline with minimized latency. Both, offline-capability and speed, are critical for applications like fraud detection and checkout automation. Accordingly, IDC emphasizes that 45% of retailers now prioritize “near-the-network” edge deployments. There, AI models run locally on in-store servers or IoT devices, balancing cost and performance.
Key Components of Edge AI Systems
For Edge AI to deliver real-time, offline-capable intelligence, its architecture must integrate on-device databases, local processing, and efficient data synchronization. These three pillars ensure seamless AI-powered retail operations without dependence on the cloud, minimizing latency, costs, and privacy concerns.
Edge AI system architecture in retail, integrating local processing, real-time data sync, and various applications like POS or signage
Retail generates vast real-time data from IoT sensors, POS transactions, smart cameras, and RFID tags. To ensure instant processing and uninterrupted availability you need:
On-device data storage: All kinds of devices from IoT sensors to cameras capture data. Depending on the device capabilities, with small on-device databases, data can be stored and used directly on the devices.
Local central server: A centralized on-premise device (e.g. a PC or Raspberry Pi, or more capable hw) ensures operations continue even if individual devices are resource-limited or offline.
Bi-directional on-premise data sync: Local syncing between devices and with a central on-site server ensures better decisions and fail-safe operations. It keeps all devices up-to-date without internet dependence.
2. Local Data Processing & Real-Time AI Decision-Making
Processing data where it is generated is critical for speed, privacy, and resilience:
On-device AI models: Small, quantized AI models (SLMs) like Microsoft’s Phi-3-mini (3.8B parameters, <2GB memory footprint) can run directly on many devices (e.g. tablets, and POS systems), enabling real-time fraud detection, checkout automation, and personalized recommendations.
Local on-premise AI models: Larger SLMs or LLMs run on the more capable in-store hardware for security, demand forecasting, or store optimization.
On-device & on-premise vector databases: AI models leverage on-device vector databases to structure and index data for real-time AI-driven insights (e.g., fraud detection, smart inventory management), fast similarity searches, and real-time decision-making.
Selective Cloud Sync: Bi-directional cloud data sync extends the on-premise data sync. Select data, such as aggregated insights (e.g., sales trends, shrinkage patterns), payment processing, and select learnings are synced with the cloud to enable Enterprise-wide analytics & compliance, Remote monitoring & additional backup, and Optimized centralized decision-making.
Cloud Database & Backend Infrastructure: A cloud-based database acts as the global repository. It integrates data from multiple locations to store aggregated insights & long-term trends for AI model refinement and enterprise reporting, facilitating cross-location comparisons.
Centralized cloud AI model: A centralized cloud AI model is optional for larger setups. It can be used to continuously learn from local insights, refining AI recommendations and operational efficiencies across all connected stores.
Use Cases of Edge AI for Retailers
Edge AI is unlocking new efficiencies for retailers by enabling real-time, offline-capable intelligence across customer engagement, marketing, in-store operations, and supply chains.
Key applications of Edge AI in retail, driving personalization, operational efficiency, and smarter decision-making.
Enhancing Customer Experiences in Retail Stores with Edge AI – Examples
Edge AI transforms the shopping experience, enabling retailers to offer more streamlined and more personalized services based on real-time data, thereby boosting customer satisfaction and sales. Key benefits include:
Contactless Checkout: AI-driven self-checkouts allow customers to select products captured by cameras. Thus, bypassing the need for scanning product codes, which streamlines both standard and automated checkout processes. For example, Amazon’s Just Walk Out technology allows customers to pick up items and leave the store without traditional checkout, enhancing convenience and reducing wait times.
Retail operational excellence and cost optimization with Edge AI – Examples
Edge AI also significantly enhances operational efficiency, especially operational in-store efficiency, reduces losses, and helps lower costs (while at the same time enhancing sustainability):
Loss Prevention: Retail shrink, exacerbated by inflation-driven shoplifting and self-checkout vulnerabilities, costs the industry over $100 billion annually. Advanced sensors and intelligent cameras combined with Edge AI help detect early signs of theft or fraud. Thus, allowing security measures to intervene promptly, and independently from an internet connection.
Energy Savings: Smart sensors and Edge AI can be used to optimize the use of energy for lighting, heating, ventilation, water use, etc. For example, 45 Broadway, a 32-story office building in Manhattan, implemented an AI system that analyzes real-time data. That included temperature, humidity, sun angle, and occupancy patterns – to proactively adjust HVAC settings. This integration led to a 15.8% reduction in HVAC-related energy consumption. Plus, saving over $42,000 annually and reducing carbon emissions by 37 metric tons in just 11 months.
Conclusion: Edge AI as Retail’s Strategic Imperative
Yet, Edge AI isn’t just about running AI models locally. It’s about creating an autonomous, resilient system where on-device vector databases, local processing, and hybrid data sync work together. This combination enables real-time retail intelligence while keeping costs low, data private, and operations uninterrupted. To stay ahead, businesses should invest in edge-ready infrastructure with on-device vector databases and data sync that works on-premise at their core. Those who hesitate risk losing ground to nimble competitors who have already tapped into real-time, in-store intelligence.
Hybrid systems, combining lightning-fast offline-first edge response times with the power of the cloud, are becoming the norm. IDC projects that 78% of retailers will adopt these setups by 2026, saving an average of $3.6 million per store annually. In an inflation-driven market, Edge AI isn’t just a perk – it’s a critical strategy for thriving in the future of retail. By leveraging Edge AI-powered on-device databases, retailers gain the speed, efficiency, and reliability needed to stay competitive in an AI-driven retail landscape.
Can Small Language Models (SLMs) really do more with less? In this article, we discuss the unique strengths of SLMs, the top SLMs, their integration with local vector databases, and how SLMs + local vector databases are shaping the future of AI,prioritizing privacy, immediacy, and sustainability.
Now, Small Language Models (SLMs) are stepping into the spotlight, enabling sophisticated AI to run directly on devices (local AI) like your phone, laptop, or even a smart home assistant. These models not only reduce costs and energy consumption but also bring the power of AI closer to the user, ensuring privacy and real-time performance.
What Are Small Language Models (SLMs)?
LLMs are designed to understand and generate human language. Small Language Models (SLMs) are compact versions of LLMs. So, the key difference between SLMs and LLMs is their size. While LLMs like GPT-4 are designed with hundreds of billions of parameters, SLMs use only a fraction of that. There is no strict definition of SLM vs. LLM yet. At this moment, SLM sizes can be as small as single-digit million parameters and go up to several billion parameters. Some authors suggest 8B parameters as the limit for SLMs. However, in our view that opens up the question if we need a definition for Tiny Language Models (TLMs)?
Advantages and disadvantages of SLM
According to Deloitte’s latest tech trends report, SLMs are gaining increasing importance in the AI landscape due to their cost-effectiveness, efficiency, and privacy advantages. Small Language Models (SLMs) bring a range of benefits, particularly for local AI applications, but they also come with trade-offs.
Benefits of SLMs
Privacy: By running on-device, SLMs keep sensitive information local, eliminating the need to send data to the cloud.
Offline Capabilities: Local AI powered by SLMs functions seamlessly without internet connectivity.
Speed: SLMs require less computational power, enabling faster inference and smoother performance.
Sustainability: With lower energy demands for both training and operation, SLMs are more environmentally friendly.
Accessibility: Affordable training and deployment, combined with minimal hardware requirements, make SLMs accessible to users and businesses of all sizes.
Limitations of SLMs
The main disadvantage is the flexibility and quality of SLM responses: SLMs typically cannot tackle the same broad range of tasks as LLMs in the same quality. However, in certain areas, they already match their larger counterparts. For example, Artificial Analysis AI Review 2024 highlights that GPT-4o-mini (July 2024) has a similar Quality Index to GPT-4 (March 2023), while being 100x cheaper in price.
Small Language Models vs LLMs
A recent study comparing various SLMs highlights the growing competitiveness of these models, demonstrating that in specific tasks, SLMs can achieve performance levels comparable to much larger models.
Overcoming limitations of SLMs
A combination of SLMs with local vector databases is a game-changer. With a local vector database, the variety of tasks and the quality of answers cannot only be enhanced but also for the areas that are actually relevant to the use case you are solving. E.g. you can add internal company knowledge, specific product manuals, or personal files to the SLM. In short, you can provide the SLM with context and additional knowledge that has not been part of its training via a local vector database. In this combination, an SLM can already today be as powerful as an LLM for your specific case and context (your tasks, your apps, your business). We’ll dive into this a bit more later.
In the following, we’ll have a look at the current landscape of SLMs – including the top SLMs – in a handy comparison matrix.
"The Gemma performs well on the Open LLM leaderboard. But if we compare Gemma-2b (2.51 B) with PHI-2 (2.7 B) on the same benchmarks, PHI-2 easily beats Gemma-2b."
iPhone 14: Phi-3-mini processing speed of 12 tokens per second. From the H2O Danube3 benchmarks you can see that the Phi-3 model shows top performance compared to similar size models, oftentimes beating the Danube3
OpenELM
270M, 450M, 1.1B, 3B
Apple
Apple License, but pretty much reads like you can do as much with it as a permissive oss license (of course not use their logo)
OpenELM 1.1 B shows 1.28% (Zero Shot Tasks), 2.36% (OpenLLM Leaderboard), and 1.72% (LLM360) higher accuracy compared to OLMo 1.2 B, while using 2× less pretraining data
"competitive performance compared to popular models of similar size across a wide variety of benchmarks including academic benchmarks, chat benchmarks, as well as fine-tuning benchmarks"
GPT-4o mini scores 82% on MMLU and currently outperforms GPT-4 on chat preferences in LMSYS leaderboard. GPT-4o mini surpasses GPT-3.5 Turbo and other small models on academic benchmarks across both textual intelligence and multimodal reasoning, and supports the same range of languages as GPT-4o
Smaller and faster variant of 1.5 Flash features half the price, twice the rate limits, and lower latency on small prompts compared to its forerunner. Nearly matches 1.5 Flash on many key benchmarks.
MMLU score of 69.4% and a Quality Index across evaluations of 53. Faster compared to average, with a output speed of 157.7 tokens per second. Low latency (0.37s TTFT), small context window (128k).
MMLU score 60.1%. Mistral 7B significantly outperforms Llama 2 13B on all metrics, and is on par with Llama 34B (since Llama 2 34B was not released, we report results on Llama 34B). It is also vastly superior in code and reasoning benchmarks. Was the best model for its size in autumn 2023.
Claimed (by Mistral) to be the world's best Edge models.
Ministral 3B has MMLU score of 58% and Quality index across evaluations of 51. Ministral 8B has MMLU score of 59% and Quality index across evaluations of 53.
Granite 3.0 8B Instruct matches leading similarly-sized open models on academic benchmarks while outperforming those peers on benchmarks for enterprise tasks and safety.
Quality Index across evaluations of 77, MMLU 85%, Supports a 16K token context window, ideal for long-text processing. Outperforms Phi3 and outperforms on many metrices or is comparable with Qwen 2.5 , and GPT-4o-mini
SLM Use Cases – best choice for running local AI
SLMs are perfect for on-device or local AI applications. On-device / local AI is needed in scenarios that involve hardware constraints, demand real-time or guaranteed response rates, require offline functionality or need to comply with strict data privacy and security needs. Here are some examples:
Mobile Applications: Chatbots or translation tools that work seamlessly on phones even when not connected to the internet.
IoT Devices: Voice assistants, smart appliances, and wearable tech running language models directly on the device.
Healthcare: Embedded in medical devices, SLMs allow patient data to be analyzed locally, preserving privacy while delivering real-time diagnostics.
Industrial Automation: SLMs process language on edge devices, increasing uptime and reducing latency in robotics and control systems.
By processing data locally, SLMs not only enhance privacy but also ensure reliable performance in environments where connectivity may be limited.
On-device Vector Databases and SLMs: A Perfect Match
Imagine a digital assistant on your phone that goes beyond generic answers, leveraging your company’s (and/or your personal) data to deliver precise, context-aware responses – without sharing this private data with any cloud or AI provider. This becomes possible when Small Language Models are paired with local vector databases. Using a technique called Retrieval-Augmented Generation (RAG), SLMs access the additional knowledge stored in the vector database, enabling them to provide personalized, up-to-date answers. Whether you’re troubleshooting a problem, exploring business insights, or staying informed on the latest developments, this combination ensures tailored and relevant responses.
Key Benefits of using a local tech stack with SLMs and a local vector database
Privacy. SLMs inherently provide privacy advantages by operating on-device, unlike larger models that rely on cloud infrastructure. To maintain this privacy advantage when integrating additional data, a local vector database is essential. ObjectBox is a leading example of a local database that ensures sensitive data remains local.
Personalization. Vector databases give you a way to enhance the capabilities of SLMs and adapt them to your needs. For instance, you can integrate internal company data or personal device information to offer highly contextualized outputs.
Quality. Using additional context-relevant knowledge reduces hallucinations and increases the quality of the responses.
Traceability. As long as you store your metadata alongside the vector embeddings, all the knowledge you use from the local vector database can give the sources.
Offline-capability. Deploying SLMs directly on edge devices removes the need for internet access, making them ideal for scenarios with limited or no connectivity.
Cost-Effectiveness. By retrieving and caching the most relevant data to enhance the response of the SLM, vector databases reduce the workload of the SLM, saving computational resources. This makes them ideal for edge devices, like smartphones, where power and computing resources are limited.
Use case: Combining SLMs and local Vector Databases in Robotics
Imagine a warehouse robot that organizes inventory, assists workers, and ensures smooth operations. By integrating SLMs with local vector databases, the robot can process natural language commands, retrieve relevant context, and adapt its actions in real time – all without relying on cloud-based systems.
For example:
A worker says, “Can you bring me the red toolbox from section B?”
The SLM processes the request and consults the vector database, which stores information about the warehouse layout, inventory locations, and specific task history.
Using this context, the robot identifies the correct toolbox, navigates to section B, and delivers it to the worker.
The future of AI is – literally – in our hands
AI is becoming more personal, efficient, and accessible, and Small Language Models are driving this transformation. By enabling sophisticated local AI, SLMs deliver privacy, speed, and adaptability in ways that larger models cannot. Combined with technologies like vector databases, they make it possible to provide affordable, tailored, real-time solutions without compromising data security. The future of AI is not just about doing more – it’s about doing more where it matters most: right in your hands.
After 6 years and 21 incremental “zero dot” releases, we are excited to announce the first major release of ObjectBox, the high-performance embedded database for C++ and C. As a faster alternative to SQLite, ObjectBox delivers more than just speed – it’s object-oriented, highly efficient, and offers advanced features like data synchronization and vector search. It is the perfect choice for on-device databases, especially in resource-constrained environments or in cases with real-time requirements.
What is ObjectBox?
ObjectBox is a free embedded database designed for object persistence. With “object” referring to instances of C++ structs or classes, it is built for objects from scratch with zero overhead — no SQL or ORM layer is involved, resulting in outstanding object performance.
The ObjectBox C++ database offers advanced features, such as relations and ACID transactions, to ensure data consistency at all times. Store your data privately on-device across a wide range of hardware, from low-profile ARM platforms and mobile devices to high-speed servers. It’s a great fit for edge devices, iOS or Android apps, and server backends. Plus, ObjectBox is multi-platform (any POSIX will do, e.g. iOS, Android, Linux, Windows, or QNX) and multi-language: e.g., on mobile, you can work with Kotlin, Java or Swift objects. This cross-platform compatibility is no coincidence, as ObjectBox Sync will seamlessly synchronize data across devices and platforms.
Why should C and C++ Developers care?
ObjectBox deeply integrates with C and C++. Persisting C or C++ structs is as simple as a single line of code, with no need to interact with unfamiliar database APIs that disrupt the natural flow of C++. There’s also no data transformation (e.g. SQL, rows & columns) required, and interacting with the database feels seamless and intuitive.
As a C or C++ developer, you likely value performance. ObjectBox delivers exceptional speed (at least we haven’t tested against a faster DB yet). Having several 100,000s CRUD operations per second on commodity hardware is no sweat. Our unique advantage is that, if you want to, you can read raw objects from “mmapped” memory (directly from disk!). This offers true “zero copy” data access without any throttling layers between you and the data.
Finally, CMake support makes integration straightforward, starting with FetchContent support so you can easily get the library. But there’s more: we offer code generation for entity structs, which takes only a single CMake command.
“ObjectBox++”: A quick Walk-Through
Once ObjectBox is set up for CMake, the first step is to define the data model using FlatBuffers schema files. FlatBuffers is a building block within ObjectBox and is also widely used in the industry. For those familiar with Protocol Buffers, FlatBuffers are its parser-less (i.e., faster) cousin. Here’s an example of a “Task” entity defined in a file named “task.fbs”:
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tableTask{
id:ulong;
text:string;
}
And with that file, you can generate code using the following CMake command:
Among other things, code generation creates a C++ struct for Task data, which is used to interact with the ObjectBox API. The struct is a straightforward C++ representation of the data model:
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structTask{
obx_id id;// uint64_t
std::stringtext;
};
The code generation also provides some internal “glue code” including the method create_obx_model() that defines the data model internally. With this, you can open the store and insert a task object in just three lines of code:
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obx::Store store(create_obx_model());// Create the database
obx::Box<Task>box(store);// Main API for a type
obx_id id=box.put({.text="Buy milk"});// Object is persisted
And that’s all it takes to get a database running in C++. This snippet essentially covers the basics of the getting started guide and this example project on GitHub.
Vector Embeddings for C++ AI Applications
Even if you don’t have an immediate use case, ObjectBox is fully equipped for vectors and AI applications. As a “vector database,” ObjectBox is ready for use in high-dimensional vector similarity searches, employing the HNSW algorithm for highly scalable performance beyond millions of vectors.
Vectors can represent semantics within a context (e.g. objects in a picture) or even documents and paragraphs to “capture” their meaning. This is typically used for RAG (Retrieval-Augmented Generation) applications that interact with LLMs. Basically, RAG allows AI to work with specific data, e.g. documents of a department or company and thus individualizes the created content.
To quickly illustrate vector search, imagine a database of cities including their location as a 2-dimensional vector. To enable nearest neighbor search, all you need to do is to define a HNSW index on the location property, which enables the nearestNeighbors query condition used like this:
This release marks an important milestone for ObjectBox, delivering significant improvements in speed, usability, and features. We’re excited to see how these enhancements will help you create even better, feature-rich applications.
As always, we’re here to listen to your feedback and are committed to continually evolving ObjectBox to meet your needs. Don’t hesitate to reach out to us at any time.
P.S. Are you looking for a new job? We have a vacant C++ position to build the future of ObjectBox with us. We are looking forward to receiving your application! 🙂
What is Edge AI?Edge AI (also: “on-device AI”, “local AI”) brings artificial intelligence to applications at the network’s edge, such as mobile devices, IoT, and other embedded systems like, e.g., interactive kiosks. Edge AI combines AI with Edge Computing, a decentralized paradigm designed to bring computing as close as possible to where data is generated and utilized.
What is Cloud AI? As opposed to this, cloud AI refers to an architecture where applications rely on data and AI models hosted on distant cloud infrastructure. The cloud offers extensive storage and processing power.
An Edge for Edge AI: The Cloud
Example: Edge-Cloud AI setup with a secure, two-way Data Sync architecture
Today, there is a broad spectrum of application architectures combining Edge Computing and Cloud Computing, and the same applies to AI. For example, “Apple Intelligence” performs many AI tasks directly on the phone (on-device AI) while sending more complex requests to a private, secure cloud. This approach combines the best of both worlds – with the cloud giving an edge to the local AI rather than the other way around. Let’s have a look at the advantages on-device AI brings to the table.
Faster Response Rates. Processing data locally cuts down travel time for data, speeding up responses.
Increased Availability. On-device processing makes apps fully offline-capable. Operations can continue smoothly during internet or data center disruptions.
Sustainability/costs. Keeping data where it is produced and used minimizes data transfers, cutting networking costs and reducing energy consumption—and with it, CO2 emissions.
Challenges of Local AI on the Edge
Data Storage and Processing: Local AI requires an on-device database that runs on a wide variety of edge devices (Mobile,IoT, Embedded) and performs complex tasks such as vector search locally on the device with minimal resource consumption.
Data Sync: It’s vital to keep data consistent across devices, necessitating robust bi-directional Data Sync solutions. Implementing such a solution oneself requires specialized tech talent, is non-trivial and time-consuming, and will be an ongoing maintenance factor.
Small Language Models:Small Language Models (SLMs) like Phi-2 (Microsoft Research), TinyStories (HuggingFace), and Mini-Giants (arXiv) are efficient and resource-friendly but often need enhancement with local vector databases for better response accuracy. An on-device vector database allows on-device semantic search with private, contextual information, reducing latency while enabling faster and more relevant outputs. For complex queries requiring larger models, a database that works both on-device and in the cloud (or a large on-premise server) is perfect for scalability and flexibility in on-device AI applications.
On-device AI Use Cases
On-device AI is revolutionizing numerous sectors by enabling real-time data processing wherever and whenever it’s needed. It enhances security systems, improves customer experiences in retail, supports predictive maintenance in industrial environments, and facilitates immediate medical diagnostics. On-device AI is essential for personalizing in-car experiences, delivering reliable remote medical care, and powering personal AI assistants on mobile devices—always keeping user privacy intact.
Personalized In-Car Experience: Features like climate control, lighting, and entertainment can be adjusted dynamically in vehicles based on real-time inputs and user habits, improving comfort and satisfaction. Recent studies, such as one by MHP, emphasize the increasing consumer demand for these AI-enabled features. This demand is driven by a desire for smarter, more responsive vehicle technology.
Remote Care: In healthcare, on-device AI enables on-device data processing that’s crucial for swift diagnostics and treatment. This secure, offline-capable technology aligns with health regulations like HIPAA and boosts emergency response speeds and patient care quality.
Personal AI Assistants: Today’s personal AI assistants often depend on the cloud, raising privacy issues. However, some companies, including Apple, are shifting towards on-device processing for basic tasks and secure, anonymized cloud processing for more complex functions, enhancing user privacy.
ObjectBox for On-Device AI – an edge for everyone
The continuum from Edge to Cloud
ObjectBox supports AI applications from Edge to cloud. It stands out as the first on-device vector database, enabling powerful Edge AI on mobile, IoT, and other embedded devices with minimal hardware needs. It works offline and supports efficient, private AI applications with a seamless bi-directional Data Sync solution, completely on-premise, and optional integration with MongoDB for enhanced backend features and cloud AI.
Interested in extending your AI to the edge? Let’s connect to explore how we can transform your applications.
As artificial intelligence (AI) continues to evolve, companies, researchers, and developers are recognizing that bigger isn’t always better. Therefore, the era of ever-expanding model sizes is giving way to more efficient, compact models, so-called Small Language Models (SLMs). SLMs offer several key advantages that address both the growing complexity of AI and the practical challenges of deploying large-scale models. In this article, we’ll explore why the race for larger models is slowing down and how SLMs are emerging as the sustainable solution for the future of AI.
From Bigger to Better: The End of the Large Model Race
Up until 2023, the focus was on expanding models to unprecedented scales. But the era of creating ever-larger models appears to be coming to an end. Many newer models like Grok or Llama 3 are smaller in size yet maintain or even improve performance compared to models from just a year ago. The drive now is to reduce model size, optimize resources, and maintain power.
The Plateau of Large Language Models (LLMs)
Why Bigger No Longer Equals Better
As models become larger, developers are realizing that the performance improvements aren’t always worth the additional computational cost. Breakthroughs in knowledge distillation and fine-tuning enable smaller models to compete with and even outperform their larger predecessors in specific tasks. For example, medium-sized models like Llama with 70B parameters and Gemma-2 with 27B parameters are among the top 30 models in the chatbot arena, outperforming even much larger models like GPT-3.5 with 175B parameters.
The Shift Towards Small Language Models (SLMs)
In parallel with the optimization of LLMs, the rise of SLMs presents a new trend (see Figure). These models require fewer computational resources, offer faster inference times, and have the potential to run directly on devices. In combination with an on-device database, this enables powerful local GenAI and on-device RAG apps on all kinds of embedded devices, like on mobile phones, Raspberry Pis, commodity laptops, IoT, and robotics.
Advantages of SLMs
Despite the growing complexity of AI systems, SLMs offer several key advantages that make them essential in today’s AI landscape:
Accessibility As SLMs are less resource-hungry (less hardware requirements, less CPU, memory, power needs), they are more accessible for companies and developers with smaller budgets. Because the model and data can be used locally, on-device / on-premise, there is no need for cloud infatstructure and they are also usable for use cases with high privacy requirements. All in all, SLMs democratize AI development and empower smaller teams and individual developers to deploy advanced models on more affordable hardware.
Cost Reduction and Sustainability Training and deploying large models require immense computational and financial resources, and comes with high operational costs. SLMs drastically reduce the cost of training, deployment, and operation as well as the carbon footprint, making AI more financially and environmentally sustainable.
On-Device AI for Privacy and Security SLMs are becoming compact enough for deployment on edge devices like smartphones, IoT sensors, and wearable tech. This reduces the need for sensitive data to be sent to external servers, ensuring that user data stays local. With the rise of on-device vector databases, SLMs can now handle use-case-specific, personal, and private data directly on the device. This allows more advanced AI apps, like those using RAG, to interact with personal documents and perform tasks without sending data to the cloud. With a local, on-device vector database users get personalized, secure AI experiences while keeping their data private.
The Future: Fit-for-Purpose Models: From Tiny to Small to Large Language models
The future of AI will likely see the rise of models that are neither massive nor minimal but fit-for-purpose. This “right-sizing” reflects a broader shift toward models that balance scale with practicality. SLMs are becoming the go-to choice for environments where specialization is key and resources are limited. Medium-sized models (20-70 billion parameters) are becoming the standard choice for balancing computational efficiency and performance on general AI tasks. At the same time, SLMs are proving their worth in areas that require low latency and high privacy.
Innovations in model compression, parameter-efficient fine-tuning, and new architecture designs are enabling these smaller models to match or even outperform their predecessors. The focus on optimization rather than expansion will continue to be the driving force behind AI development in the coming years.
Conclusion: Scaling Smart is the New Paradigm
As the field of AI moves beyond the era of “bigger is better,” SLMs and medium-sized models are becoming more important than ever. These models represent the future of scalable and efficient AI. They serve as the workhorses of an industry that is looking to balance performance with sustainability and efficiency. The focus on smaller, more optimized models demonstrates that innovation in AI isn’t just about scaling up; it’s about scaling smart.
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.
Figure 1. Evolution of language model’s size with time. Large language models (LLMs) are marked as celadon circles, and small language models (SLMs) as blue ones.
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-device 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).
Figure 2. Interest over time according to Google Trends.
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:
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 to 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. According to McKinsey, the demand for data center capacity is projected to grow by over 20% annually, reaching approximately 300GW by 2030, with 70% of this capacity dedicated to hosting AI workloads. Gartner even predicts that by 2025, “AI will consume more energy than the human workforce”. 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.
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