[On-Device AI Chatbot] Part 1: Why "On-Device AI" Now? (Overview)

Why "On-Device AI" Now?

Over the past few years, generative AI, led by services like ChatGPT, has revolutionized our daily lives and workflows. However, behind these powerful AI services lies a common limitation: cloud dependency. The standard architecture—where user queries are sent to cloud servers and the computed results from massive data centers are sent back—inevitably introduces risks such as data privacy breaches, network latency, and exorbitant server maintenance costs.

To overcome these limitations and bring the locus of intelligence directly to the hardware, "On-Device AI" (or Edge AI) is rapidly emerging as a new paradigm. In this first installment, we will explore why the AI trend is shifting from the cloud to the edge, and the background behind why TecAce decided to launch its own on-device AI chatbot project.

3 Core Innovations Brought by On-Device AI

Going far beyond a simple "chatbot that works without the internet," modern on-device AI functions as a complete generative assistant. Here are the three primary reasons why enterprises and developers are turning their attention to on-device AI:

1. Ultimate Privacy and Data Security As regulatory pressures such as Europe's GDPR and the EU AI Act intensify, data security has become a matter of corporate survival. Using cloud-based AI means sensitive information must be transmitted to external servers, exposing organizations to the risk of data-in-flight breaches. In contrast, on-device AI performs all inference and data retrieval entirely within the user's local device (e.g., smartphones, tablets). Because model parameters and user data never travel over a network, the probability of external data leaks is fundamentally eliminated.

2. Ultra-Low Latency and Resilience A typical cloud LLM API call can experience a round-trip delay of 800 to 900 milliseconds or more, depending on network conditions. Humans typically notice conversational lag starting at around 250ms and feel irritated when it exceeds 600ms. On-device AI suffers from zero network jitter or server queue delays, meaning the device's compute speed equals the response speed. By leveraging modern NPUs (Neural Processing Units) like Qualcomm's Snapdragon X Elite, it is possible to achieve near-instantaneous responses of around 400ms for a seamless conversational interface. Furthermore, it guarantees 100% operational resilience even in environments where internet connectivity is unstable or entirely unavailable.

3. Massive Reduction in Cloud Operating Expenses (OPEX) Using commercial cloud APIs (like GPT-4o) incurs continuous billing based on token usage, often averaging around $15 per million tokens. For environments generating millions of tokens monthly, this can easily translate to thousands of dollars in recurring annual costs. On-device AI, however, requires only the initial hardware investment, eliminating ongoing cloud API fees and drastically reducing long-term operating expenses (OPEX).

TecAce Launches the On-Device AI Chatbot Project

Driven by these powerful advantages of on-device AI, the TecAce team launched the 'On-device AI chat-bot' project to innovate our internal communication environment.

Project Objective Currently, many enterprises rely heavily on external communication tools for their daily operations. This reliance inherently carries the risk of leaking internal corporate information, source code, and confidential documents to the outside world. To reduce this external dependency and bolster data security, TecAce set out to build an internal communication platform that is secure, scalable, and user-friendly.

Project Scope

• Real-time Messaging & File Sharing: Development of an in-house real-time messaging system supporting text, image, and file sharing.

• Fully Offline Secure Chatbot: Integration of an AI model that runs directly on the user's mobile device (Android), requiring absolutely no external cloud servers or internet connection.

• Unified Platform: Evolving into a single collaboration platform capable of supporting a large user base and incorporating advanced analytics features in the future.

This chatbot, which thinks and responds entirely within the device, is not the rule-based offline bot of the past. The core challenge of this project is to implement an optimized Small Language Model (SLM) and push the device's NPU resources to their absolute limits, replicating the reasoning capabilities of massive cloud models right in the palm of your hand.

Next Episode Preview

"So, how do you fit these massive language models into a smartphone?" To build a fast and smart AI within the strict memory and battery constraints of a smartphone, selecting the right Small Language Model (SLM) is the most critical first step.

In the upcoming [Part 2] Giant Language Models in the Palm of Your Hand: SLM Selection Strategy, we will analyze the ecosystem of the latest SLMs—such as Google Gemma 3 and Meta Llama 3.2—and share the vivid details of the criteria TecAce used to test and select the final model for our project.