5 min read
In February 2023, Qualcomm stunned the tech world at Mobile World Congress with a groundbreaking demo: Stable Diffusion. It turned out to be a popular text-to-image generative AI model, running entirely on an Android smartphone for the first time. It was a glimpse into a future where powerful AI could live in our pockets, free from cloud dependency.
Fast-forward to February 2026, and that vision has matured into a reality that’s transforming how we interact with our devices. On-device generative AI was once slow, experimental, and power-hungry. Now, in many aspects, it powers instant, privacy-focused experiences like real-time image creation, proactive assistants, and multimodal content generation.
Let’s dive into a “now vs. then” exploration into the hardware, software, and ecosystem shifts that have propelled this explosive growth, highlighting contributions from leaders like Qualcomm, Apple, and Google.
Then: The Dawn of On-Device Generative AI (2023)
Back in 2023, generative AI was exploding in popularity thanks to tools like ChatGPT and DALL-E, but running these models on mobile devices seemed like science fiction. Cloud servers handled the heavy lifting, requiring constant internet and raising privacy concerns as user data zipped back and forth.
Qualcomm’s demo changed that narrative. Using the Snapdragon 8 Gen 2 chip (found in flagships like the Samsung Galaxy S23) they optimized Stable Diffusion v1.5 (a 1-billion-parameter model from Hugging Face) to generate a 512×512 pixel image from a text prompt in under 15 seconds. Techniques like quantization (reducing model precision) and hardware acceleration via the Hexagon NPU made it possible, but it was far from seamless.
The process took about 20 inference steps, felt clunky compared to cloud speeds, and drained battery quickly. Early independent ports on older chips, like Snapdragon 865, were even slower: up to an hour per image.
At the time, on-device AI was limited to basic tasks: simple photo editing, voice recognition, or object detection. Generative capabilities were nascent, with Google’s Tensor G3 (in Pixel 8) offloading most complex AI to the cloud. Apple’s Neural Engine in A16/A17 chips handled efficient ML but focused more on photography enhancements than full-blown generation. Challenges abounded: memory constraints (phones had 8–16GB RAM), power efficiency (NPUs topped ~10–20 TOPS), and model sizes that ballooned computational needs.
Now: Instant, Agentic, and Ubiquitous Gen AI (2026)
By 2026, on-device generative AI has leaped from demos to core features, driven by hardware that packs 50–80 TOPS of AI performance, optimized software stacks, and a shift toward “agentic” AI. New AI systems are proactive systems that anticipate needs and act autonomously. Generative AI apps now rank among the top five mobile categories by downloads, revenue, and time spent, projected to exceed $10 billion in consumer spending. Over 80% of mobile interactions leverage AI for personalization and automation.
Qualcomm’s Snapdragon Leadership
The Snapdragon 8 Elite Gen 5 (honored at CES 2026) runs on-device LLMs and multimodal models with ~60 TOPS, generating images in under a second, down from 15 seconds in 2023. Snapdragon X2 Plus (Jan 2026) extends this to PCs and robotics, enabling agentic AI like proactive assistants that manage apps without prompts. Partnerships with Google bring generative AI to cars, like voice-driven interfaces that adapt in real-time. Dragonwing chips push into humanoid robotics, blending gen AI with physical actions.
Apple’s Neural Engine Dominance
The M5 chip (Oct 2025) features a 16-core Neural Engine with 153GB/s memory bandwidth, boosting on-device AI by 15% over M4. Apple Intelligence runs locally for privacy, powering features like Image Playground (faster gen AI creation) and Siri upgrades. A19 Pro hits ~35 TOPS, supporting frameworks like MLX for on-device LLMs. Reports affirm Apple’s “local AI bet” pays off, with agents using personal data securely on-device.
Google’s Tensor Integration
Tensor G5 (in Pixel 10, 2025) runs the latest Gemini Nano for multimodal AI (text, image, audio, speech), enabling 20+ on-device gen AI experiences like real-time voice translation and Magic Cue (proactive info). It helps Pixel weather a projected 7% market dip in 2026. Features include 100x Pro Res Zoom with gen AI upscaling.
Broader ecosystem
Edge computing and SLMs (small language models) make AI offline-capable, reducing latency and costs. Gen AI matures into hyper-personalization, with 63% of organizations adopting globally. Phones like Galaxy S25/S26 integrate agentic AI for tasks like app orchestration.
How Far We’ve Come: A Side-by-Side Comparison
- Speed and Efficiency: Then: 15 seconds for a basic image; power-hungry. Now: Sub-second generation; 4x GPU compute on chips like Tensor G5/M5, with multi-day battery thanks to optimized NPUs (35–80 TOPS vs. 10–20 in 2023).
- Model Capabilities: Then: Single-modal (text-to-image); 1B params. Now: Multimodal (text/image/audio); agentic systems chaining tasks, running LLMs offline for privacy.
- Accessibility and Integration: Then: Flagship-only demos. Now: Standard on mid-range devices; OS-level features (e.g., Android’s agentic shift, iOS 19’s Apple Intelligence expansions).
- Privacy and Use Cases: Then: Cloud-reliant, data risks. Now: On-device focus minimizes leaks; expands to agentic apps in health, automotive, robotics.
The leap stems from hardware-software co-design: chips tailored for AI, frameworks like ExecuTorch/Metal 4, and regulations pushing edge computing.
In 2026, generative AI evolves into agentic systems, i.e., AI that reasons, plans, and acts. Expect smartphones as “AI companions” in AR/VR, smart homes, and vehicles. However ethical AI, energy use, accessibility challenges remain without a proper solution in sight. Still, from Qualcomm’s 2023 spark to 2026’s blaze, on-device gen AI proves innovation thrives at the edge, literally in our hands.
