Is it the year on on-device vector databases yet? Or at least on-device AI?
A year ago the interest in “on-device vector database” (also “local vector DB”, or “edge vector DB”) was mostly theoretical / experimental. While we saw SLMs appearing and rapdily dropping in size while gaining in capability, the market overall was not ready, even though vector database technology was already powering Apple Intelligence and shipped to very iPhone.
The only thing “missing” is the on-device memory layer: Vector databases engineered for phones, ECUs, and other restricted edge devices. Ok, it’s not entirely missing, but the on-device vector database is a genuinely small field of products engineered for phones and ECUs. And we’re going to take a deeper look into that market in this article.
Note: A lot of brands still claiming “on-device” are, in practice, running on a high-end developer laptop that is off Wi-Fi. This article is focused primarily on local AI on more restricted devices like e.g. smartphones, ECUs, and PoS systems.
Why now?
Well, a lot of things happened in parallel, finally allowing for on-device AI on a larger scale:
SLMs like Gemma 3n, Phi-3, Qwen 2.5 now run usefully under ~4 GB. Retrieval can therefore be paired with on-device generation without destroying the device’s memory budget.
Vectors got smaller: A 1,536-dimensional float32 vector is 6 KB. Quantized to 1 bit per dimension it is 192 bytes – a ~32× memory reduction with typical recall loss in the ~5–10% range depending on the model and reranking strategy.
The cloud cost conundrum[1] became real: Gartner and IDC, both, report rapidly growing cost numbers for cloud and AI infastructure and expect further climbs. IDC FutureScape 2026 warns that Global 1000 organisations will under-estimate their AI infrastructure costs by ~30% through 2027.
Privacy regulations came into effect: The EU AI Act Article 5 prohibitions came into force in early February 2025, general-purpose AI obligations from August 2025, and full enforcement is scheduled for 2 August 2026.
What “on-device” actually means
A “real” on-device / edge (or mobile) vector DB for Edge AI persists locally, supports vector + metadata/hybrid search, exposes mobile-usable SDKs (Java / Swift / Kotlin / Flutter) for Mobile and C / C++ for other embedded devices, handles incremental CRUD, has predictable and efficient RAM/storage, a small footprint, works offline, and ideally supports selective data sync. ANN indexing math is the easy part – the hard part is mobile lifecycle, thermal throttling, encrypted storage, and sync of derived data when source content changes. E.g. Faiss is a solid library and good for some use cases, but it is not a database. Let’s look at what’s out there and which criteria they currently meet.
What is an Edge Database?
Edge databases are a type of databases that are optimised for local data storage on restricted devices, like embedded devices, Mobile, and IoT. Because they run on-device, they need to be especially resource-efficient (e.g. with regards to battery use, CPU consumption, memory, and footprint). The term “edge database” is becoming more widely-used every year, especially in the IoT industry. In IoT, the difference between cloud-based databases and ones that run locally (and therefore support Edge Computing) is crucial.
What is a Mobile Database?
We look at mobile databases as a subset of edge databases that run on mobile devices. The difference between the two terms lies mainly in the supported operating systems / types of devices. Unless Android and iOS are supported, an edge database is not really suited for the mobile device / smartphone market. In this article, we will use the term “mobile database” only as “database that runs locally on a mobile (edge) device and stores data on the device”. Therefore, we also refer to it as an “on-device” database.
Vendor Map
We only cover options that can plausibly run on resource-constrained devices here. You can find more on general vector databases here, though that review is from 2024 and due to AI / the development of search we did not found it worthwhile updating. The on-device vector database is worth covering as it is only shaping and lacking broad coverage. Approximate footprint shown — always verify on your target hardware.
Segment
Vector Database
Approx. footprint
Sync
ACID
Metadata filter
Benchmarks / Efficiency
Status
Dedicated mobile / embedded DBs with vectors (vector search)
Note: Excluded due to size / minimum requirements or availability: Qdrant Edge announced July 2025 as a re-architected in-process variant (private beta, partner-curated); not publicly available; the publicly distributed Qdrant is a server (~900MB compiled binary). Milvus Lite — Python binary, Linux/macOS only; broader Milvus typically provisioned with multiple GB RAM. DuckDB VSS — analytics-class; ≥125MB RAM/thread minimum, 1–4GB/thread for optimal performance. SQL Server 2025 — server-class: ≥1GB RAM (Express) / ≥4GB (other editions), ≥6GB disk, x64 only.
Why “edge vector database” tech is different from cloud
Most of the columns above probably look familiar from any other database review. The reason this category is genuinely different from typical databases, and cloud / server vector databases in particular, comes down to four things:
Strict resource limits. In the cloud, performance problems can often be solved by scaling horizontally, adding memory, or moving to a larger instance. On a physical device, the compute, RAM, flash etc. are fixed. That changes the underlying architecture and the dilligence required in development: indexing, query execution, persistence, and sync all need to be efficient by design rather, because you cannot compensate with “throwing resources at the problem”.
Energy budgets matter. On battery-powered devices, every query, write, compaction, sync, and re-embedding job also competes with the user experience, thermal limits, and battery life – constraints a cloud database usually does not face directly (more costs though…).
The edge is fragmented. “Edge” can mean a smartphone, an ECU, a PoS terminal, a Linux gateway, an industrial PC, or a microcontroller-class device. These systems vary widely in operating system, CPU architecture, storage, available RAM, update model, security requirements, and connectivity. A credible edge vector database therefore needs more than ANN search; it needs predictable behavior across constrained and heterogeneous environments.
Sync is hard. I would say harder than search. Vectors are derived data. When source content changes, permissions change, or the embedding model is upgraded, old embeddings may become stale. An edge vector database therefore needs to handle not only local search, but also updates, deletes, re-indexing, and selective sync between device and cloud. This is where a database matters more than a standalone ANN library.
Do you actually need an on-device vector database? When?
As always: It depends. Use on-device vector DBs when (basically when you need Edge Computing):
you have privacy requirements; data is personal; you face compliance needs
the app needs to work when offline, or reliably under flaky network conditions
you want speed (think UX) or you need quaranteed response times (QoS)
you need to cut networking and cloud costs to make the economics work
Let’s look at some cases where on-device vector databases are truly needed.
Mobile Apps
The strongest mobile use case currently isn’t generic “AI on phones,” but private assistant memory and context for RAG-based apps: AI chats or assistants that can answer questions using personal, app-specific, or domain-specific knowledge, for example in travel, product support, field service, or maintenance.
Notes, messages, files, photos, app activity, preferences, and location-specific history are already on the device. An on-device vector database lets an assistant embed that context locally into an on-device vector DB, retrieve it instantly, and sync only selected data when needed. That makes the experience faster and more private, while keeping the app useful even when connectivity is poor.
Domain-specific knowledge is often not publicly available to a general-purpose AI model. It may only exist inside an app, a downloaded manual, a product catalog, or a company’s technical documentation. In those cases, the app can use this semantic context through a local vector database. For example, a maintenance assistant could store heating-system technical docs on the phone, identify a part or problem from a photo, retrieve the relevant repair instructions, and suggest targeted fixing steps. Added benefit: it still works in the cellar.
Vehicles / ECUs
Vehicles are a strong fit because software-defined vehicles need cloud-scale learning, but in-car execution cannot depend on perfect connectivity. McKinsey says automotive software and electronics are moving toward zonal and central compute architectures for OTA updates, connectivity, and gen AI, with the market reaching $519 billion by 2035. The vector DB role is a compact local memory layer for in-cabin assistants, offline manual search, driver personalization, predictive diagnostics, and retrieval over vehicle logs or VSS-normalized signals. McKinsey’s edge-AI survey reinforces the hybrid stance: stakeholders cited offline availability (39%), latency (35%), privacy/security (20%), and network data cost (6%) as main factors for moving AI onboard; they also flagged SoC constraints (46%) and energy consumption (35%) as limits on what can run in the vehicle. So the answer is not cloud vs. edge; it is local-first retrieval and selective sync to the backend. This is the same position as the ObjectBox / MongoDB architecture: ObjectBox handles low-latency local operations and bi-directional sync connects selected data to MongoDB Atlas for storage, analytics, retraining, and coordination.
Point-of-sales systems
PoS systems often work on premises with flaky network conditions and offline and hybrid payment models improve payment resilience, accepting cash and card payments offline and uploading them after reconnection. A local vector layer makes sense when the PoS wants to improve the service and customer experience with AI features, e.g. with semantic lookup over products, promotions, policies, allergens, prior orders etc. 67% of retail executives expect AI-driven personalization capabilities in 2026, and McKinsey’s 2026 retail research says AI is reshaping discovery and purchase behavior as stores remain important. The pattern is local operations first, cloud analytics later: answer routine queries instantly in-store, then sync selected sales, stock, customer, and personalization data when the network is available.
Bottom line
The bottom line: on-device vector databases are moving from “interesting idea” to a much needed enabler for local AI. Not every app needs one, and many workloads will stay cloud-first, but y hybrid AI approach combining the best of the edge and the cloud is often benefitial. Whenever data is private, latency-sensitive, cost-sensitive, or needed offline, pushing vector search to the device makes a lot of sense. On top, finding the right balance between on-device AI and cloud AI helps save costs, and energy, and is therefore economically and environmentally the most sustainable option. The hard part is not just an ANN search, which a small dedicated lib can easily do; it is efficient persistence, updates, deletes, metadata filtering, sync, footprint, and predictable behavior under real device constraints. If we predict the future from the past, shrinking large server / cloud vector databases to work on edge devices will not work. Instead, this market needs dedicated and highly optimized solutions. Therefore, we believe, it will be won by databases actually engineered for the edge.
What happens when two offline devices edit the same thing?
ObjectBox Sync now gives developers more control over concurrent updates. With the latest Sync Server release and updated Dart/Flutter and Java clients, developers can choose how conflicting writes are resolved in offline-first and distributed applications. The feature is available with ObjectBox Dart/Flutter 5.3.1, ObjectBox Java/Kotlin/Android 5.4.1, and Sync Server 2026-03-26.
What is ObjectBox Sync? ObjectBox Sync is an offline-first sync engine that keeps data consistent across devices and backend systems, even with unreliable connectivity. It supports user-specific sync for personalized data, customizable conflict resolution, and MongoDB integration for backend connectivity.
When multiple devices update the same object, ObjectBox Sync can now resolve conflicts using two new mechanisms: Sync Clock and Sync Precedence. Sync Clock is managed automatically by ObjectBox Sync and tracks the “time” for which write should win. Sync Precedence is controlled by the developer and lets business rules decide which write is more important. These options can be used independently or together. When both are present, precedence is evaluated first, and the sync clock acts as the tie-breaker.
The new ObjectBox Sync Clock was designed for offline-first systems. It is an advanced hybrid logical clock (HLC) that combines wall time with a logical counter. That means writes can still be ordered consistently even when devices are offline, reconnect at different times, or have some clock skew. The clock tracks real time as closely as possible, never goes backwards, and uses extra compensation for clocks set in the future, making it particularly robust for concurrent offline edits. This gives developers a sensible default for real-world sync scenarios. A write that was actually made later can win even if it reaches the server earlier or later than another update.
For teams that need more than time-based ordering, Sync Precedence adds another layer of control. Developers can assign a precedence value to an object and ensure that higher-precedence changes win in a conflict. That makes it possible to encode workflow and authority directly into synchronization behavior. A closed order can stay closed even if a newer edit arrives that was still based on the open state. An approval state can override a draft state. A manager’s correction can take precedence over a regular user update.
The combination of both approaches is especially powerful. Developers can use precedence to represent business intent, and let the sync clock resolve ties fairly and automatically. The result is conflict resolution that is both application-aware and offline-friendly.
For developers building collaborative, field, retail, logistics, or other edge applications, this update removes a major source of friction in distributed data handling. You can now decide whether conflicts should be resolved by when a change happened, by how important that change is, or by a combination of both.
Availability
ObjectBox Sync customizable conflict resolution is available now with:
ObjectBox 5.0 delivers the most requested updates across the board. If you are building an offline-first application and need a seamless Data Sync solution, we believe, this is the upgrade you have been waiting for:
New Sync Filters for true user-specific data sync (GA)
A new MongoDB Sync Connector (GA)
5.0 database/client releases for Dart, Java/Kotlin, Swift, C, and C++
Better examples, stability improvements, and quality-of-life fixes
Smarter Sync: user-specific and with MongoDB
The big news is all around ObjectBox Sync and the two major new features: user-specific sync filters and connecting to MongoDB. After working closely with select customers for the last months, we are happy to announce the general availability for both features.
With 5.0, you can now define Sync Filters to control exactly which data each Sync user receives.
Define filter expressions on the server that run per user
Use auth/JWT and client-provided variables inside those filters
Enable “each user only sees their own data” without duplicating data or maintaining separate partitions
For the new MongoDB Sync Connector, we’ve partnered with MongoDB to create a tight integration:
Sync your data from and to MongoDB in “real time”
Edge setup for multiple locations: deploy one ObjectBox Sync Server per location, all syncing to one central MongoDB
Integrate ObjectBox-powered apps with an existing MongoDB database or analytics pipeline
This brings the best of both worlds: a fast, embedded offline-first database for your mobile, IoT, or embedded apps, and a central MongoDB store for backend integration, reporting, and other services. Best of all, you don’t need a custom application backend – the ObjectBox Sync Server handles the heavy lifting, keeping your app data in sync with MongoDB automatically.For more information, check our MongoDB page or the MongoDB Sync Connector documentation.
Migrating from Realm Device Sync?
If you are looking for an alternative to the deprecated MongoDB Realm Device Sync, ObjectBox is the natural choice. Like Realm, ObjectBox is object-oriented, making migrating from Realm to ObjectBox straightforward and fast. You get the same offline-first capabilities and out-of-the-box Data Sync you know plus: industry-leading speed and efficiency.
The ObjectBox database is known for its extremely high CRUD performance and vector search for AI use cases. It can be used as a standalone embedded database or in combination with ObjectBox Sync. As it is closely integrated into programming languages to offer native object persistence, the 5.0 release spans multiple releases:
All 5.0 Sync clients are compatible with the new Sync Filters and MongoDB Sync Connector. Check the release links above for language-specific improvements.
Further reading and links
There has never been a better time to build with ObjectBox. Here is how to get started:
Sync examples: includes a tasks application with clients for all platforms and a preconfigured Sync Server. Also includes a Docker Compose that boots up a MongoDB to sync with; it’s set up in a minute, give it a try!
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.
Ever waited to order or pay with a waiter holding their ordering device in the air for a signal? These moments show why offline-first Data Sync is essential. With more and more services relying on the availability of on-device apps and the IoT market projected to hit $1.1 trillion by 2026, choosing the right solution – particularly online-only or offline-first data sync – is more crucial than ever. In this blog, we discuss their differences and highlight common Data Sync alternatives.
What is Data Sync?
Data synchronization (Sync) aligns data between two or more devices to maintain consistency over time. It is an essential component in applications ranging from IoT and mobile apps to cloud computing. Challenges in data synchronization include asynchrony, conflicts, and managing data across flaky networks.
Data Sync vs. Data Replication
Data Synchronization is often confused with Data Replication. Nevertheless, they serve different purposes:
Data Replication: A unidirectional process (works in one direction only) that duplicates data across storage locations to ensure availability and prevent loss. It is simple but limited in its application, and efficiency, and lacks conflict management.
Data Synchronization: A bidirectional process that harmonizes all or a subset of data between two or more devices. It ensures consistency across devices and entails conflict resolution. It is inherently more complex but also more flexible.
Online vs Offline Solutions: Why Offline Sync Matters
Online-only synchronization solutions rely entirely on cloud infrastructure, requiring a stable internet connection to function. While these tools offer simplicity and scalability, their dependency on constant cloud connectivity brings limitations: Online Data Sync solutions cannot guarantee response rates and their speed varies depending on the network. They do not work when offline or in on-premise settings. Using an Online Sync solution often entails sharing the data and might not comply with data privacy requirements. So, do read the terms and conditions.
Offline-first solutions (offline Sync) focus on local data storage and processing, ensuring the app remains fully functional even without an internet connection. When a network is available, the app synchronizes seamlessly with a server, the cloud, or other devices as needed. These solutions are ideal for on-premise scenarios with unreliable or no internet access, mission-critical applications that must always operate, real-time and high-performance use cases, as well as situations requiring high data privacy and data security compliance.
A less discussed, but in our view also relevant point, is sustainability. While there might be exceptions depending on the use case, for most applications offline-first solutions are more resourceful and therefore more sustainable. If CO2 footprint or battery usage is of concern to you, you might want to look into offline-first Data Sync alternatives.
The CE Sync is a bare minimum and typically not usable; Self-hosted Sync with Couchbase Servers is available as part of their Enterprise offering
✅ as part of the Enterprise offering; gets expensive quickly
Edge: Couchbase Lite; Server: Couchbase
Multi-model NoSQL document-oriented database
Couchbase Lite: iOS, Android, macOS, Linux, Windows, Raspbian and Raspberry Pi OS
Couchbase Sync Gateway: Red Hat Enterprise Linux (RHEL) 9.x, Alma Linux 9.x, Rocky Linux 9.x, Ubuntu, Debian (11.x, 12.x), Windows Server 2022
.Net C Go Java JavaScript info Kotlin PHP Python Ruby Scala
✅
Couchbase Lite is available under different licenses; the open source Community Edition does not get regular updates and misses many features especially around Sync (e.g. it does not include Delta Sync making it slow and expensive)
Typically requires Couchbase servers, quickly gets expensive
MongoDB: NoSQL document store; RealmDB: Embedded NoSQL DB
MongoDB: Linux OS X Solaris Windows Mongo Realm DB: Android, iOS
more than 20 languages, e.g. Java, C, C#, C++
✅
MongoDB changed its license from open source (AGPL) to MongoDB Inc.’s Server Side Public License (SSPL) in 2018. RealmDB is open source under the Apache 2.0 License. The Data Sync was proprietary.
While SQLite does not offer a sync solution out-of-the-box, various vendors have built something on top, or integrated with SQLite giving them offline persistence.
Key Considerations for Choosing a Data Sync Solution
When selecting a synchronization solution, consider:
Connectivity Requirements: Will the application function in offline environments; how will it work with flaky network conditions; how is the user experience when there is intermittent connectivity?
Data Privacy & Security: How critical is it to ensure sensitive data remains local? Data compliance? How important is it that data is not breached?
Scalability and Performance: What are the expected data loads and network constraints? How important is speed for the users? Is there any need to guarantee QoS parameters? How much will the cloud and networking costs be?
Conflict Resolution: How does the solution handle data conflicts?
Delta Sync: Does the solution always synchronize all data or only changes (data delta)? Can a subset of data be synchronized? How efficient is the Sync protocol (affecting costs and speed)?
The Shift Towards Edge Computing
The trend toward Edge Computing highlights the growing preference for offline-first solutions. By processing and storing data closer to its source, Edge Computing reduces cloud dependency, enhances privacy, and improves efficiency. Data synchronization plays an important role in this shift, ensuring seamless operation across decentralized networks.
Offline and online synchronization solutions each have their merits, but the rise of edge computing and data privacy concerns has propelled offline Sync to the forefront. Developers must assess their application’s unique requirements to select the most appropriate synchronization method. As the industry evolves, hybrid and offline-first solutions are going to dominate, offering the best balance of functionality, privacy, and performance.
Can Small Language Models (SLMs) really do more with less? In this article, we discuss the unique strengths of SLMs, learn about the top SLMs, 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.
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