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
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 are already matching 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.
Inference pricing by intelligence tier adapted from Artificial Analysis AI Review 2024. Quality is the Artificial Analysis Quality Index.
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.
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.
In today’s fast-paced, decentralized world valuable data is generated by everything, everywhere, and all at once. To harness the vast opportunities offered by this data for data-driven organizations and AI applications, you need to be able to access the data and seamlessly distribute it to when and where it’s needed.
The key to achieving this lies in efficient, offline-first on-device data storage, reliable bi-directional data sync, and a scalable data management backend in the cloud. In other words, you need the infrastructure to manage data flows bi-directionally to tap into fresh data throughout your organization, processes, and applications at the right time.
Together, MongoDB and ObjectBox provide developers with a robust solution to empower seamless workload and data flows on the edge and from the edge to the cloud. ObjectBox seamlessly syncs data bi-directionally across devices even without Internet and syncs back to the cloud and MongoDB when connected. With ObjectBox devices stay in sync also in environments with intermittent connectivity, high latency, or flaky networks. Capture and unlock the value of all your data, anytime, anywhere, without relying on a constant Internet connection, with MongoDB + ObjectBox.
Seamless Offline-First Data Sync for Edge Devices
Maintaining service continuity is essential, even when devices are offline. Your customers, users, operations, and employees need to be able to rely on essential data at all times. That’s where ObjectBox comes in. It comprises of two key components: the ObjectBox Database and ObjectBox Data Sync.
The ObjectBox Database is a lightweight, on-device solution that is highly resource-efficient and fast on restricted hardware like mobile, IoT, and embedded devices, and even in the cloud.
ObjectBox Data Sync enables seamless bi-directional data synchronization between devices. By handling only incremental changes in a compressed binary format, ObjectBox Sync ensures minimal data transfer, automatic conflict resolution, and a seamless user experience even in fluctuating network conditions. This approach effectively simplifies the development process by offering complex sync logic via easy native-language APIs, allowing developers to focus on core app functionality.
Once a connection is available, ObjectBox Data Sync instantly synchronizes changes with MongoDB, providing real-time, bi-directional data sync between edge devices and MongoDB’s robust cloud backend.
The Benefits of Offline-First and Real-Time Data Sync with MongoDB and ObjectBox:
Resource-efficiency & Highspeed: ObjectBox excels at consuming minimal computational resources (CPU, power, memory, …) while delivering data persistence speed that is typically on-par with in-memory caches for read operations.
Offline-First Operation: Ensure continuous app performance, even with no internet connection. ObjectBox stores and syncs data bi-directionally on the edge and additionally with MongoDB once connected.
Real-Time Data Sync: Get reliable, bi-directional data synchronization across devices and MongoDB, enabling real-time updates and data consistency.
Scalable Edge: Easily handle 100k operations / second on a single device. Host the Sync server on any edge device (like a phone) and easily handle 3M clients with a three-node cluster.
Scalable Cloud Backend: With MongoDB, businesses can scale their applications to handle growing data and performance demands, seamlessly syncing data between millions of devices and the cloud.
Flexible Setup Scenarios: Tailor Data Sync to Your Needs
ObjectBox and MongoDB offer flexible setup scenarios to meet different application needs. The two main setup options are the central sync and the edge sync setup.
The Central Sync Setup syncs data between edge devices and MongoDB in the cloud, providing centralized data management while retaining offline-first functionality. The ObjectBox Sync Server runs in the cloud or on-premise.
The Edge Sync Setup allows devices to operate and sync data efficiently offline between ObjectBox instances within an edge, e.g. within one location, or within a car. When reconnected, changes are synchronized back to MongoDB making it ideal for environments with intermittent connectivity or distributed devices that need to function independently while syncing back to the cloud when possible.
This structure offers a flexible approach to integrating edge and cloud systems, empowering organizations to choose the setup that best fits their specific use case. More details.
Use Cases for MongoDB + ObjectBox :
Data-Driven Organizations: In a data-driven organization, every decision relies on access to relevant, up-to-date data. ObjectBox enables real-time data collection and synchronization from edge devices, ensuring access to critical data, even when devices are intermittently connected. This streamlines operations, improves decision-making, and enhances analysis across distributed teams and IoT systems. With MongoDB’s scalable cloud infrastructure, decentralized data integrates seamlessly with the cloud backend for efficient management.
Point-of-Sale (PoS) & Retail Edge Computing: A seamless customer experience and the ability to keep selling and never lose a transaction, even during internet outages, are essential for PoS systems / in retail. ObjectBox enables offline-first data storage and syncing for PoS systems, allowing transactions to be processed locally, even without internet connectivity. When connectivity returns, ObjectBox syncs transaction data back to MongoDB in real time, ensuring data consistency across multiple locations. Retailers can then leverage MongoDB’s analytics to gain insights into customer behavior and optimize inventory management.
Software-Defined Vehicle (SDV) & Connected Cars: Modern vehicles generate vast amounts of data from sensors and onboard systems. ObjectBox enables efficient on-device storage and processing, providing real-time access to data for navigation, diagnostics, and infotainment systems. ObjectBox Data Sync ensures that local data is synced back to MongoDB when connectivity is available, supporting centralized analytics, fleet management, and predictive maintenance, optimizing performance and safety while enhancing the user experience.
Manufacturing & Smart Shopfloor Apps: In smart factories, machines and sensors continuously generate data that must be analyzed and processed in real time. ObjectBox enables local data storage and fast data sync on-premise without the necessity for an Internet connection, ensuring that critical systems that are not connected to the Internet can run smoothly on-site. With a connected instance, ObjectBox takes care of synchronizing this data with the cloud and MongoDB for further analysis and central dashboards.
AI-Applications with On-device Vector Search: ObjectBox is the first and only on-device vector database, empowering developers to run AI locally on mobile, IoT, embedded, and other commodity devices (Edge AI). In combination with a Small Language Model (SLM), this allows developers to build local AI applications (e.g. RAG, genAI) that run directly on the device—without needing a cloud connection. By syncing with MongoDB, businesses can combine the power of on-device AI with centralized cloud data for even greater insights and performance. This is especially beneficial in scenarios requiring real-time decision-making, such as personalized customer experiences and predictive maintenance.
In today’s data-driven world, a data-first strategy requires seamless integration between edge and cloud data management. The combination of MongoDB and ObjectBox unlocks the full potential of your data. MongoDB’s powerful cloud platform, together with ObjectBox’s efficient on-device database and offline-first capabilities, is ideal for capturing the value of your data from anywhere, including distributed edge devices where valuable data is generated all the time. This partnership empowers businesses to seamlessly handle decentralized data, enabling fast and reliable operations at the edge while syncing back to the cloud for centralized management. Whether on IoT devices, mobile, embedded systems, or commodity hardware, ObjectBox and MongoDB ensure optimal performance everywhere. From remote areas to bad networks, our joint solution keeps data flowing reliably between the edge and the MongoDB backend, even when connectivity or nodes are lost.
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.
Easily empower your iOS and macOS apps with fast, private, and sustainable AI features. All you need is a Small Language Model (SLM; aka “small LLM”) and ObjectBox – our on-device vector database built for Swift apps. This gives you a local semantic index for fast on-device AI features like RAG or GenAI that run without an internet connection and keep data private.
The recently demonstrated “Apple Intelligence” features are precisely that: a combination of on-device AI models and a vector database (semantic index). Now, ObjectBox Swift enables you to add the same kind of AI features easily and quickly to your iOS apps right now.
Typical AI apps use data (e.g. user-specific data, or company-specific data) and multiple queries to enhance and personalize the quality of the model’s response and perform complex tasks. And now, for the very first time, with the release of ObjectBox 4.0, this will be possible locally on restricted devices.
Local AI Tech Stack Example for on-device RAG
Swift on-device Vector Database and search for iOS and MacOS
With the ObjectBox Swift 4.0 release, it is possible to create a scalable vector index on floating point vector properties. It’s a very special index that uses an algorithm called HNSW. It’s scalable because it can find relevant data within millions of entries in a matter of milliseconds. Let’s pick up the cities example from our vector search documentation. Here, we use cities with a location vector and want to find the closest cities (a proximity search). The Swift class for the City entity shows how to define an HNSW index on the location:
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// objectbox: entity
classCity{
varid:Id=0
varname:String?
// objectbox:hnswIndex: dimensions=2
varlocation:[Float]?
}
</code><!--wp:paragraph--><!--/wp:preformatted-->
Inserting City objects with a float vector and HNSW index works as usual, the indexing happens behind the scenes:
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let box:Box<city>=store.box()
trybox.put([
City("Barcelona",[41.385063,2.173404]),
City("Nairobi",[-1.292066,36.821945]),
City("Salzburg",[47.809490,13.055010]),
])
</city></code><!--wp:paragraph--><!--/wp:html-->
To then find cities closest to a location, we do a nearest neighbor search using the new query condition and “find with scores” methods. The nearest neighbor condition accepts a query vector, e.g. the coordinates of Madrid, and a count to limit the number of results of the nearest neighbor search, here we want at max 2 cities. The find with score methods are like a regular find, but in addition return a score. This score is the distance of each result to the query vector. In our case, it is the distance of each city to Madrid.
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let madrid=[40.416775,-3.703790]// query vector
// Prepare a Query object to search for the 2 closest neighbors:
The ObjectBox on-device vector database empowers AI models to seamlessly interact with user-specific data — like texts and images — directly on the device, without relying on an internet connection. With ObjectBox, data never needs to leave the device, ensuring data privacy.
Thus, it’s the perfect solution for developers looking to create smarter apps that are efficient and reliable in any environment. It enhances everything from personalized banking apps to robust automotive systems.
ObjectBox: Optimized for Resource Efficiency
At ObjectBox, we specialize on efficiency that comes from optimized code. Our hearts beat for creating highly efficient and capable software that outperforms alternatives on small and big hardware. ObjectBox maximizes speed while minimizing resource use, extending battery life, and reducing CO2 emissions.
With this expertise, we took a unique approach to vector search. The result is not only a vector database that runs efficiently on constrained devices but also one that outperforms server-side vector databases (see first benchmark results; on-device benchmarks coming soon). We believe this is a significant achievement, especially considering that ObjectBox still upholds full ACID properties (guaranteeing data integrity).
Cloud/server vector databases vs. On-device/Edge vector databases
Also, keep in mind that ObjectBox is a fully capable database. It allows you to store complex data objects along with vectors. Thus, you have the full feature set of a database at hand. It empowers hybrid search, traceability, and powerful queries.
Use Cases / App ideas
ObjectBox can be used for a million different things, from empowering generative AI features in mobile apps to predictive maintenance on ECUs in cars to AI-enhanced games. For iOS apps, we expect to see the following on-device AI use cases very soon:
Across all categories we’ll see Chat-with-files apps:
Travel: Imagine chatting to your favorite travel guide offline, anytime, anywhere. No need to carry bulky paper books, or scroll through a long PDF on your mobile.
Research: Picture yourself chatting with all the research papers in your field. Easily compare studies and findings, and quickly locate original quotes.
Lifestyle:
Health: Apps offering personalized recommendations based on scientific research, your preferences, habits, and individual health data. This includes data tracked from your device, lab results, and doctoral diagnosis.
Productivity: Personal assistants for all areas of life.
Family Management: Interact with assistants tailored to specific roles. Imagine a parent’s assistant that monitors school channels, chat groups, emails, and calendars. Its goal is to automatically add events like school plays, remind you about forgotten gym bags, and even suggest birthday gifts for your child’s friends.
Professional Assistants: Imagine being a busy sales rep on the go, juggling appointments and travel. A powerful on-device sales assistant can do more than just automation. It can prepare contextual and personalized follow-ups instantly. For example, by summarizing talking points, attaching relevant company documents, and even suggesting who to CC in your emails.
Educational:
Educational apps featuring “chat-with-your-files” functionality for learning materials and research papers. But going beyond that, they generate quizzes and practice questions to help people solidify knowledge.
Run the local AI Stack with a Language Model (SLM, LLM)
Recent Small Language Models (SMLs) already demonstrate impressive capabilities while being small enough to run on e.g. mobile phones. To run the model on-device of an iPhone or a macOS computer, you need a model runtime. On Apple Silicone the best choice in terms of performance typically MLX – a framework brought to you by Apple machine learning research. It supports the hardware very efficiently by supporting CPU/GPU and unified memory.
To summarize, you need these three components to run on-device AI with an semantic index:
ObjectBox: vector database for the semantic index
Models: choose an embedding model and a language model to matching your requirements
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