Storage speed is emerging as a critical limiting factor for on-device artificial intelligence (AI) performance, and a wave of new hardware and software solutions is designed to remove this bottleneck. Until recently, most attention in edge AI focused on processing power, but as large language models and multimodal AI systems move onto phones, tablets, and edge devices, the ability to quickly move data between storage and compute has become just as important as raw computing speed (Source 1, 2, 3).

Why Is Storage Speed Suddenly Critical for Local AI?

On-device AI workloads have fundamentally changed what storage systems need to do. Traditional mobile storage was optimized for apps and media files, which don't require constant, high-speed data access. But large language models, image generation, video synthesis, and robotics inference all demand rapid movement of massive amounts of data between storage and the processor. When storage can't keep up, the entire system slows down, no matter how powerful the processor is.

"On-device AI is fundamentally changing storage requirements. As AI models become larger and more sophisticated, storage performance has become a critical enabler of overall system responsiveness," said Maitry Dholakia, Vice President of Memory Business Unit at Kioxia America, Inc.

Maitry Dholakia, Vice President, Memory Business Unit, Kioxia America, Inc.

Kioxia, a major flash memory manufacturer, announced new UFS 5.0 embedded flash memory solutions designed specifically to address this challenge. The new devices deliver sequential read performance of up to 10 gigabytes per second and write performance of up to 9 gigabytes per second, representing a significant leap from previous generations. These speeds enable faster application loading, greater responsiveness, and what Kioxia describes as "an unprecedented user experience" for on-device AI applications compared to the previous UFS 4.1 standard.

What Devices Will Benefit From Faster Storage?

While premium smartphones with on-device AI are the primary target, the applications extend far beyond phones. Kioxia's UFS 5.0 is also suited for AI-enabled tablets, gaming systems, augmented reality and virtual reality devices, robotics, smart security cameras, and industrial edge applications that require fast local storage for data-intensive AI workloads. The compact 7.5 by 13 millimeter package size makes it practical for space-constrained devices, and it will be available in 1 terabyte and 512 gigabyte capacities, with mass production expected to begin by the end of 2026.

Meanwhile, developers building on-device AI applications are gaining new tools to take advantage of faster storage. Tether AI released QVAC SDK 0.16, a software development kit that enables local execution of advanced multimodal AI models directly on consumer and industrial hardware. The toolkit includes on-device image generation via Ideogram 4, synchronized video with audio through LTX-2, and local execution of NVIDIA's GR00T robotics model, all without relying on cloud services.

How Are Developers Simplifying On-Device AI Deployment?

• Preconfigured Development Environments: InHand Networks launched the Mo 62A and Mo 68A AI single-board computers with preconfigured development environments that handle camera input, video processing, on-device inference, and inference result output, reducing setup and integration work for developers.
• Cross-Platform Compatibility: Tether's QVAC SDK enables developers to write code once in JavaScript or TypeScript and deploy across mobile and desktop operating systems without platform-specific rewrites, while supporting diverse GPU architectures from AMD, Intel, and Apple.
• Hardware Agnosticism: The toolkit provides OpenAI-compatible local APIs for existing tools and leverages Vulkan graphics technology to support different hardware vendors, reducing vendor lock-in and lowering barriers to entry for developers.
• Vision AI Optimization: InHand's boards support MIPI CSI camera input, seventh-generation image signal processing, and H.264 and H.265 video codecs, with the Mo 62A offering 2 TOPS (trillion operations per second) and the Mo 68A offering 8 TOPS for different application complexity levels.

The Mo 62A and Mo 68A boards are designed to help teams move from model validation to product prototyping with less foundational configuration and debugging. They support Debian, Texas Instruments EdgeAI SDK, OpenCV, and GStreamer, which are commonly used development components for model deployment and inference result handling.

What Are the Real-World Benefits of Local AI Execution?

Running AI models locally rather than in the cloud offers multiple practical advantages. Privacy improves because data stays on the device instead of being transmitted to remote servers. Latency decreases dramatically since there's no network round-trip delay. Operational costs drop because developers avoid per-prompt API subscription fees. And reliability increases because local execution doesn't depend on internet connectivity or cloud service availability.

For robotics applications specifically, local execution of models like NVIDIA's GR00T mitigates latency in physical robotics by ensuring near-native speeds and lowering dependence on unstable internet connections. This is critical for autonomous systems that need to make real-time decisions without waiting for cloud responses.

The shift toward local-first AI also addresses a broader industry challenge. The global generative AI market is projected to grow from 55.51 billion dollars in 2026 to approximately 1.2 trillion dollars by 2035, expanding at a compound annual growth rate of 36.97 percent. As this market scales, the ability to run sophisticated AI workloads without constant cloud dependency becomes increasingly valuable for cost management and operational resilience.

Storage performance improvements and developer-friendly toolkits represent a maturation of the on-device AI ecosystem. Rather than treating local AI as a secondary option to cloud processing, the industry is now optimizing every layer, from flash memory speed to software frameworks, to make on-device inference the primary path for AI applications. This shift reflects a fundamental recognition that the future of AI isn't just about model size or processing power, but about the entire system's ability to move data efficiently and execute complex workloads without external dependencies.