Qualcomm is taking an unusual approach to the launch of its next-generation premium Snapdragon mobile platform this year. Instead of holding everything for its annual Snapdragon Summit in Maui happening later this month, the company has been methodically disclosing the architecture that will power the next wave of flagship Android phones over the past couple of weeks.

First, the company disclosed its new Oryon CPU architecture, followed by a major overhaul of its Adreno GPU. Today, Qualcomm is detailing the next-generation Hexagon NPU that will serve as the platform’s primary AI engine.

Taken together, the disclosures give us a pretty good picture of where Qualcomm is going before we even know the chip’s official name.

There is obviously a strong performance story here, anchored by a 5GHz CPU, but the more interesting theme running through the architecture is memory locality and specialized AI acceleration. Qualcomm is trying to keep more data close to the compute resources that need it, while distributing AI workloads across the CPU, GPU and NPU.

These architectural changes are important as the company continues to drive on-device AI beyond relatively simple generative features toward more persistent, agentic workloads.

Oryon hits 5GHz, bolstered by FlexCache

Qualcomm disclosed in August that its next Oryon CPU will be the first mobile CPU to reach 5GHz. The eight-core design includes two 5GHz Prime cores and six Performance cores, with Qualcomm attributing the frequency gains to its fully custom Oryon microarchitecture, an in-house Arm-based CPU design that gives the company full control over the cores, cache and memory hierarchy, branch prediction and CPU subsystem.

The 5GHz headline is certainly going to get attention. However, Qualcomm’s new FlexCache architecture may ultimately have a larger impact on sustained performance. FlexCache allows Oryon’s Prime and Performance cores to tap into the same shared cache pool, with capacity dynamically allocated based on the workload. So rather than a demanding core being limited to a fixed slice of cache, it can draw on more of the available pool when needed. This keeps larger working data sets close to the CPU cores and reduces high-latency trips out to system memory.

This approach should benefit everything from gaming and multitasking to content creation, but it also aligns well with agentic AI workloads, where tasks may move through several stages of processing and across different CPU cores. The basic goal is straightforward: keep the CPU fed and avoid expensive trips to external memory.

Qualcomm’s next-gen Adreno GPU adds dedicated AI engines

Qualcomm’s next disclosure focused on graphics, where the company is making one of the more significant changes to its Adreno GPU engine in recent years. The new GPU adds dedicated Adreno Matrix Cores for running AI models directly inside the graphics pipeline. They’re paired with 18MB of Adreno High-Performance Memory, or HPM, which provides low-latency local storage for tile-based rendering, frame buffers and GPU compute.

Qualcomm claims HPM provides a 12% power improvement compared to its previous-gen Snapdragon 8 Elite Gen 5.

The more visible feature for users, however, may be Adreno Neural Fusion, which combines AI super resolution, neural processing and frame generation in a unified graphics pipeline. There are obvious parallels here to what NVIDIA, AMD and others are doing with neural rendering on the PC, though Qualcomm’s next-gen Snapdragon SoC has to operate within a much tighter mobile power envelope.

Qualcomm claims enabling Neural Fusion reduces power consumption by up to 40% in its internal testing using the company’s Dragon Alley graphics demo. That’s an impressive number, though as always, we’ll want to see how the technology behaves across actual shipping games and devices before drawing any definitive conclusions.

Qualcomm has also helped integrate Neural Fusion technology into Unity and Unreal Engine, which may ultimately be just as important as the underlying hardware. Developer adoption determines whether features like this become meaningful platform advantages or just another tick box on a spec sheet.

Qualcomm’s Hexagon NPU is being reworked for agentic AI

Qualcomm

The most recent disclosure centers on Qualcomm’s Hexagon NPU, where the broader architecture starts to come together. The company’s next-generation Hexagon introduces a new Element Accelerator designed for transformer workloads, alongside its existing vector and scalar processing resources. Qualcomm says the new block is optimized for fast action loops, key-value (KV) cache acceleration and context lengths of up to 32K.

Hexagon is also getting 50% more shared memory, and again, that memory complement is important. Model state, context and KV-cache data can generate substantial memory traffic, particularly with larger language models. Keeping more of that data close to the NPU can reduce latency and power consumption. Qualcomm says the architecture is designed for long-context reasoning, multimodal models, concurrent agents and low-latency action loops.

For INT4 quantized models, the company claims up to a 50% improvement in pre-fill performance versus its previous-generation Snapdragon 8 Elite Gen 5. Qualcomm is also targeting Mixture-of-Experts, or MoE, models as large as 30 billion parameters. In its example, the company points to a 30B model that activates only about 3 billion parameters during a given token-generation step. That selective approach is a natural fit for the tight power and memory constraints of a handset.

Qualcomm isn’t suggesting that a smartphone will simply run a 30B dense model entirely from DRAM, however. By activating only the experts needed for a particular task, MoE architectures can dramatically reduce active compute and memory bandwidth requirements, potentially making much larger classes of AI models practical on mobile hardware.

Qualcomm’s next-gen Snapdragon mobile impact

There is a larger competitive story here as well. For years, flagship smartphone competition largely came down to CPU, GPU, modem performance and power efficiency. AI is now another major area of differentiation, and Qualcomm is clearly designing its next-gen Snapdragon mobile platform around this need.

Apple has the advantage of controlling its silicon, operating system and software stack end-to-end. MediaTek continues to push aggressively into premium Android devices as well. Qualcomm’s response is to make its custom Oryon CPU, Adreno GPU and Hexagon NPU work more like a fully integrated compute platform, tuned for modern AI and agentic workloads.

This is a solid advantage for Android handset OEMs because many don’t have the resources to engineer this level of silicon integration themselves. Qualcomm can effectively provide its Android OEM partners, including Samsung, Xiaomi, Honor and others, with a common hardware foundation for on-device AI, advanced graphics and agentic computing. It also gives Qualcomm another way to defend its premium Snapdragon position.

The company’s custom Oryon architecture now spans smartphones and Windows PCs, and soon servers, while AI acceleration is distributed across its CPU, GPU and NPU. For business users, that could mean more AI processing happens locally, reducing cloud and network dependence, improving response times and keeping more potentially sensitive data on-device. And for IT organizations, Qualcomm now has an opportunity to provide a more consistent AI compute foundation across Windows PCs with Snapdragon X and premium Android handsets.

There is still plenty we don’t know, however. Qualcomm hasn’t disclosed the complete SoC specifications, final performance or power characteristics, or which devices will ship with it initially. Many of these AI experiences will also depend heavily on Android, app developers and the models themselves.

The new 5GHz mobile CPU may generate the biggest headline at Snapdragon Summit, but the more important developments may be what sit around it: more local memory, specialized AI acceleration and tighter integration between the CPU, GPU and NPU.

If Qualcomm can translate this architecture into better sustained performance, enhanced power efficiency and useful on-device AI experiences, it could strengthen its position in premium Android phones while putting more competitive pressure on Apple. For business users, the payoff could be more capable local AI without sacrificing the performance and battery life expected of a flagship device.

That is the part I’ll be watching most closely when the complete silicon picture is unveiled at Snapdragon Summit later this month. And I may even get some hands-on time with it in some benchmarks, as we have in years past, so we shall see.

By now, you probably know my stance on Gemini and other generative AI gobbledegook — right?

In short: It’s an insanely powerful form of technology (obviously). But it’s also wildly inaccurate and ineffective as an all-purpose answer engine, and by cramming it into every last nook and cranny, Google is doing a serious disservice to its users — and to itself.

That being said, generative AI can be incredibly useful. The key is simply to frame it as a purpose-specific tool for the right type of limited task as opposed to treating it as the end-all answer for everything, as most tech players seem overly eager to do these days.

Here in the land of Android, I’m always looking for purpose-specific ways Gemini can make our lives easier. Over time, I’ve found those ideas often aren’t the big headline-making features Google and other tech companies market but rather random little off-the-beaten-path possibilities that rarely get emphasized.

Today, I’ve got a new Gemini Android advantage for you to explore — another one that I’ve never seen advertised anywhere but that works brilliantly well and solves a long-standing limitation of our modern mobile gizmos.

It’ll take you all of a minute to master and cost you precisely $0 to pull off. Ready?

[Get next-level knowledge in your inbox with my free Android Intelligence newsletter — one useful new thing to try every Friday, straight from me to ye.]

Gemini’s custom sound advantage

This Gemini-provided power-up very much follows the path of thinking about AI as a limited, purpose-specific tool for a narrow task — the exact sort of area where AI excels.

I won’t keep you waiting: Gemini, as I discovered whilst poking around within it recently, is really good at generating unique, custom ringtones and notification sounds based on your personal preferences and specifications.

It almost seems obvious, once you think about it. But I sure hadn’t thought about it up until now — and I suspect most other folks haven’t, either.

The advantage here goes beyond just superficial silliness, too: By creating your own custom ringtones and notification noises, you can know exactly what event your phone is alerting you to within a split second of hearing the associated sound — without having to rely on the same limited and often underwhelming pool of default options that everyone else uses (or, worse yet, having to lean on shady, ad-ridden “ringtone apps” to find mediocre alternatives). That means you’ll know within a heartbeat when your boss or an especially important client is calling or if an incoming call is a random unknown number.

On the notification front, you can create a specific, memorable sound to accompany a new message in an important work-related Slack channel and a different distinctive sound for a high-priority email. They’ll all be sounds that you make and identity with and that no one else in the world uses. That’s pretty powerful.

And creating these custom sounds couldn’t be much easier, either, once you realize it’s possible:

First, just fire up Gemini on your phone (or on any device you’re using) and tell it what you want. So, for instance…

• Create a soft, subtle ringtone that’s reminiscent of an old office phone sound.
• Create a ringtone that sounds like an 8-bit version of The Final Countdown.
• Create a one-second high-pitched notification sound that brings to mind the Super Mario Bros theme song.
• Create a short notification sound that mimics the Back to the Future time circuits noise.
• Create a notification sound that sounds like a robot saying “ALERT, ALERT!”

The only limit is your imagination. And if you’re not feeling especially imaginative, you can even ask Gemini to give you a list of ideas for distinctive, memorable ringtones or notification sounds based on geeky nostalgia, 80s pop-metal, or whatever it is that tickles your fancy.

Once you’ve sent a request, Gemini will — somewhat confusingly — spit back a big honkin’ block of HTML code. This is because, for reasons unknown, the system can’t or won’t just provide a sound file directly. Go figure.

JR Raphael, Foundry

But from there, it’s just one more quick step to get the file you need: Click or tap the copy icon in the upper-right corner of that code block — the icon that looks kinda like two stacked, rounded rectangles — to copy the entire chunk of code onto your system clipboard.

Then open up your browser and go to the website JSFiddle.net. It’s a well-known, long-standing tool for running code like HTML right in your browser and seeing the result, and it works entirely in the browser and without any downloads or special permissions required.

Tap or click into the “HTML” field, then paste in the code from your clipboard (by long-pressing any open space, on Android, and selecting “Paste” from the menu that appears). Click or tap the “Run” button, and — ta-da: You’ll see a player to listen to your custom Gemini-generated sound and a button to download it.

JR Raphael, Foundry

Provided you like what you hear, hit the button to download the file, then head into the Sound section of your Android system settings to save it and make it available for applying as any kind of alert or ringtone. The exact steps for doing that will vary from one type of Android device to another, but on a Pixel or another phone that follows Google’s core Android interface, you’ll tap on either “Ringtone” or “Notification,” then tap “My Sounds” and tap the plus icon to import your own custom file from your phone’s local storage.

On a Samsung device, you’ll start the same way but look for the plus icon within the “Ringtone” section of the settings, then tap the “Folders” tab to find your locally downloaded files. Samsung doesn’t make it easy to add in your own custom notification sounds, annoyingly, so what you’ll have to do is open up the Google Files app, find the file you downloaded in your “Downloads” folder, then tap the three-dot icon alongside it to copy it to the folder called “Notifications.” Once the file is in that folder, it should show up as a notification noise option within the Samsung Android settings.

And remember: You can use Android’s notification channels to set specific sounds to be used for different types of alerts, even within a single app — and you can use the Google Contacts app to set custom ringtones for different people, too.

With Gemini as your personal composer, you’ll never be stuck with generic overused sounds again — and you’ll always know exactly what’s happening from the first note of whatever noise you hear.

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