The Edge AI Advantage

It is scary how much data we share with AI models. Not talking about that AI is trained on data that is scraped from the web, but simply what we share with them. Chatting with an AI every day to help you debug your code, suggest movies and books, help plan your next vacation, and more. By now, which ever chatbot you use knows more about you than Google and Facebook does based on your browsing history. And now that agentic AI is becoming more mainstream, this will only get worse.

We wrote last time that the adoption rate of AI is affected by how much we trust it. No one will enter their credit card details into an AI agent if there’s the risk that the AI will either buy the wrong items at the wrong price, or enter your details on a scam website, or that openAI has access to your details through the chat history. OpenAI, Anthropic and others spend a lot of time and money into safety of their models, but there is another way of building trust. That is by using small language models that are running directly on your device.

This is not the only benefit, running locally also means that you can use the model without an internet connection, and have almost no latency. For the big tech companies, there is the additional benefit that sending everything to the cloud doesn’t scale nicely.

These small language models run with under 1 billion parameters, while still achieving surprisingly high performance. Meta’s Llama 3.2-1B and Microsoft’s Phi-3.5-Mini for example perform only a few percent worse on benchmarks compared to the flagship models while using only 1% of the computational power.

Besides us regular consumers that are concerned with privacy and safety, these small models are extremely valuable in fields like healthcare, manufacturing and self-driving cars. Most countries have very strict privacy laws concerning patient data, and by analyzing data locally you can ensure compliance while providing real-time diagnosis. In manufacturing, when data security is important or when an assembly line is in a remote location, local models are a must. Lastly, self-driving cars can use local models to make decisions in milliseconds, without waiting for a cloud response.

Four techniques make all this possible:

1. knowledge distillation: where large “teacher” models train smaller “student” models
2. strategic pruning: removing redundant neural pathways
3. precision quantization: reducing numerical precision without meaningful accuracy loss
4. specialized chips: NVIDIA’s Jetson series delivers 275 TOPS of AI performance, while Qualcomm builds AI processing directly into mobile chips.

What’s Coming Next?

Reports say that by 2028, 54% of mobile devices will be AI‑capable.

• Default local assistants: A majority of voice, keyboard, and photo features will execute on‑device with sub‑1B models quantized to 4–8‑bit.
• Hybrid compute norm: Devices run most steps locally and burst to cloud only for harder tasks, balancing privacy, latency, and cost.
• Enterprise adoption: Regulated industries shift many AI workflows to on‑device/hybrid for compliance and auditability, especially in healthcare, finance, and automotive.
• Multimodal on-device: Speech, vision, and sensor fusion become always‑on via NPUs; wake‑word reliability and context windows improve with efficient KV caching.
• Personalization without data leave: On‑device adapters (e.g., LoRA/IA3) and federated learning deliver private personalization without sharing raw data.
• Edge hardware ubiquity: NPUs ship across phones, laptops, and embedded boxes; schedulers split work across CPU/GPU/NPU with battery‑aware policies.
• Local RAG toolchains: Lightweight vector stores and embedding pipelines run locally; OSes expose APIs for private retrieval and sandboxed browsing.
• Format and provenance: Interop around GGUF/CoreML/ONNX improves; app stores add model provenance/attestation and signed updates.
• Economics: TCO shifts as on‑device inference reduces cloud spend; “cloud‑only for spikes” becomes a common pattern.
• Risk surface: More attention to device‑side prompt injection, model drift, and energy budgets; standardized eval packs and signed tool calls emerge.