Google Quietly Launches AI Edge Gallery: Run AI Models Locally on Your Phone

Google quietly released an app that lets you download and run AI models locally | TechCrunch

Last week, Google quietly released an app that lets users run a range of openly available AI models from the AI dev platform Hugging Face on their phones. Called Google AI Edge Gallery, the app is available for Android and will soon come to iOS. It allows users to find, download, and run compatible models that generate images, answer questions, write and edit code, and more. The models run offline, without needing an internet connection, tapping into supported phones' processors. AI models running in the cloud are often more powerful than their local counterparts, but they also have their downsides. Some users might be wary of sending personal or sensitive data to a remote data center, or want to have models available without needing to find a Wi-Fi or cellular connection. Google AI Edge Gallery, which Google is calling an "experimental Alpha release," can be downloaded from GitHub by following these instructions. The home screen shows shortcuts to AI tasks and capabilities like "Ask Image" and "AI Chat." Tapping on a capability pulls up a list of models suited for the task, such as Google's Gemma 3n. Google AI Edge Gallery also provides a "Prompt Lab" users can use to kick off "single-turn" tasks powered by models, like summarizing and rewriting text. The Prompt Lab comes with several task templates and configurable settings to fine-tune the models' behaviors. Your mileage may vary in terms of performance, Google warns. Modern devices with more powerful hardware will predictably run models faster, but the model size also matters. Larger models will take more time to complete a task -- say, answering a question about an image -- than smaller models. Google's inviting members of the developer community to give feedback on the Google AI Edge Gallery experience. The app is under an Apache 2.0 license, meaning it can be used in most contexts -- commercial or otherwise -- without restriction.

This new Google app lets you use AI on your phone without the internet - here's how

You want to use AI on your phone, but you're stuck in a dead zone. Typically, that won't work, as those large language models require an internet connection. Aah, but wait. Google has cooked up an app designed to surmount that barrier. Available now for Android and coming soon to iOS, the experimental Google AI Edge Gallery will download the necessary models and files required to use an AI. You can then carry out the usual tasks, such as asking questions and finding information, even if you're offline. Also: Your Google Gemini assistant is getting 8 useful features - here's the update log "The Google AI Edge Gallery is an experimental app that puts the power of cutting-edge Generative AI models directly into your hands, running entirely on your Android (available now) and iOS (coming soon) devices," Google said on the app's GitHub page. "Dive into a world of creative and practical AI use cases, all running locally, without needing an internet connection once the model is loaded. Experiment with different models, chat, ask questions with images, explore prompts, and more." The setup process is awkward, but doable. Since it's experimental, Google AI Edge Gallery isn't available through Google Play or other official app stores. Instead, you'll have to install it through an APK file. For that, you'll need to tweak the settings on your Android device to allow the installation of unknown apps. Plus, you must have an account with Hugging Face, through which you download the LLM versions that will run locally. "On your device, head to Settings and select the setting for Apps. Depending on your phone and OS version, either tap the option at the bottom for "Special app access" or tap the three-dot icon at the top and select "Special access. At the next screen, tap the option for "Install unknown apps" and then turn on the switch for Chrome to allow from that source. Also: How to try Veo 3, Google's AI video generator that's going viral on the internet To grab the app, head to the GitHub page through Chrome on your Android device. Swipe down the page past the screenshots and tap the link for "Download the App: Grab the latest APK." If you receive a notification telling you that the file might be harmful, tap the option to download it anyway. Tap the Open link, and then tap Install. After the installation, tap Open to launch the app. For future use, you'll also find the app's icon in your app library. To get you started, the app offers three example tasks to try -- Ask Image, AI Chat, and Prompt Lab. Tap any of them. You'll then have to choose one or more of the LLMs to download and use. Here, the app offers different versions of Google's Gemma LLM, a lightweight and open model similar to Gemini, but able to run locally. Tap the double arrow next to any version to learn more about it. Otherwise, select one and tap "Download & Try." To proceed, you'll need to create or sign into a Hugging Face account. After logging in, you'll have to agree to the license terms. That process can be clumsy, but don't give up. The version you choose is downloaded and installed on your device. A couple of them are several gigabytes in size, so be patient. You can then download other versions if you wish. Also: 3 ways Google's AI Mode will change how you shop online - if it works When done, return to the main Google AI Edge Gallery screen and tap one of the three examples. Select the downloaded model you wish to use, and then tap Try. Choose AI Chat, for example, and then type or speak your prompt. In response, the AI will generate its response. I asked it when and where pizza was invented, and the AI delivered a detailed response. Next, you may want to try Ask Image. Here, you can upload an image from your device or take a new photo and then ask questions about it. However, I ran into some hiccups when I tried this. I took a photo of a model of the USS Enterprise from the original Star Trek and asked the AI how many crew members served aboard. The AI correctly identified the ship, but kept coming up with wildly inaccurate numbers for the crew count, like 22 and 10 (the actual number is around 430). After I called it out on each mistake, it apologized and said that it was still under development and still learning. I even tried different models, but it continued to give me wrong answers. Also: How to run a local LLM as a browser-based AI with this free extension I also showed it a picture of the cover of Amazing Spider-Man #1 and asked it to identify the comic book. On the first attempt, it informed me that it was Amazing Spider-Man #2. When I told it that answer was wrong, it then served up the right information. Beyond the hallucinations, the app itself crashed a few times when I was working with the Ask Image prompt. Of course, AI Edge Gallery is labeled an experiment for a reason. Don't expect a stable performance or accurate answers across the board. But if Google is able to fine-tune the app, then it could pave the way for AIs that can run effectively whether you're online or not.

This Google experimental app lets you run powerful AI models without Wi-Fi

Forget Google Keep, OneNote and Evernote, 5 reasons NotebookLM is the future of note-taking Summary Google has quietly launched an experimental app called AI Edge Gallery, allowing users to run AI models on their devices without needing a Wi-Fi or cellular connection. The app supports several open-source AI models from platforms like Hugging Face, including Google's own Gemma 3n. The app is currently available for download on Android devices and is coming soon to iOS. Whether you're the one rushing to your tech-savvy friend just to add a widget to your lock screen, or you are that friend, keeping up with how fast AI tools are developing isn't possible for the average person anymore. Google's among the many companies trying to make their mark in the AI world. For instance, Google I/O 2025, which was held just a couple of days ago, focused primarily on AI (to absolutely no one's surprise). Related I gave up on AI -- until Android XR's potential won me back I see the light Posts The company has also been constantly rolling out new AI models, introducing new AI-powered features, and even recently teased Gemini on the Pixel Watch. At this point, there's clearly no stopping Google. Though the Mountain View-based company has been quite vocal about its AI developments for the most part, there are some things it chooses to roll out quietly. Well, we just got our latest example of Google doing exactly that and if you're into local AI, you're going to want to pay attention. Google quietly releases AI Edge Gallery for Android devices As reported by TechCrunch, Google quietly rolled out an app, Google AI Edge Gallery, that lets users run AI models on their devices without needing a Wi-Fi connection. The app, which is currently available for Android only, allows users to use several "openly available AI models" from Hugging Face, like Google's Gemma 3n. If you're not aware of what Hugging Face is, it's an AI development platform that provides machine learning models to developers for building applications. It hosts open-source, pre-trained machine learning models and grants developers easy access to them. On the app's GitHub page, Google mentions that the experimental app "puts the power of cutting-edge Generative AI models directly into your hands." Google's new AI app lets users hunt down openly available AI models, download them, and then run them locally without being connected to Wi-Fi. When running them locally, users can ask the AI to do pretty much everything they'd normally do when connected to Wi-Fi, like generating images, chatting with it, browsing the web, coding, and asking questions. Image Credit: Google Though they obviously won't be able to do everything that typical AI models requiring an internet connection can do, local AI models still have quite a few benefits. For starters, local AI models give faster responses since there's no lag caused by waiting for responses from a server. This is because local AI models harness the power of your device's processor and everything runs on-device, not in the cloud. Since nothing essentially leaves your device, there's a significantly lower risk of your data being intercepted, stored, or misused. Ultimately, local AI models are a great choice for privacy-conscious users. Of course, another obvious benefit you may have figured out by now is that you won't need to find a stable Wi-Fi or cellular connection to use local AI models. As mentioned above, Google AI Edge Gallery can currently be downloaded on Android devices. It's not available on the Play Store just yet, but you can get it from GitHub using the official user guide and by downloading the APK. Google also mentioned that it's coming soon for iOS devices as well.

Google quietly launches AI Edge Gallery, letting Android phones run AI without the cloud

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google has quietly released an experimental Android application that enables users to run sophisticated artificial intelligence models directly on their smartphones without requiring an internet connection, marking a significant step in the company's push toward edge computing and privacy-focused AI deployment. The app, called AI Edge Gallery, allows users to download and execute AI models from the popular Hugging Face platform entirely on their devices, enabling tasks such as image analysis, text generation, coding assistance, and multi-turn conversations while keeping all data processing local. The application, released under an open-source Apache 2.0 license and available through GitHub rather than official app stores, represents Google's latest effort to democratize access to advanced AI capabilities while addressing growing privacy concerns about cloud-based artificial intelligence services. "The Google AI Edge Gallery is an experimental app that puts the power of cutting-edge Generative AI models directly into your hands, running entirely on your Android devices," Google explains in the app's user guide. "Dive into a world of creative and practical AI use cases, all running locally, without needing an internet connection once the model is loaded." The application builds on Google's LiteRT platform, formerly known as TensorFlow Lite, and MediaPipe frameworks, which are specifically optimized for running AI models on resource-constrained mobile devices. The system supports models from multiple machine learning frameworks, including JAX, Keras, PyTorch, and TensorFlow. At the heart of the offering is Google's Gemma 3 model, a compact 529-megabyte language model that can process up to 2,585 tokens per second during prefill inference on mobile GPUs. This performance enables sub-second response times for tasks like text generation and image analysis, making the experience comparable to cloud-based alternatives. The app includes three core capabilities: AI Chat for multi-turn conversations, Ask Image for visual question-answering, and Prompt Lab for single-turn tasks such as text summarization, code generation, and content rewriting. Users can switch between different models to compare performance and capabilities, with real-time benchmarks showing metrics like time-to-first-token and decode speed. "Int4 quantization cuts model size by up to 4x over bf16, reducing memory use and latency," Google noted in technical documentation, referring to optimization techniques that make larger models feasible on mobile hardware. The local processing approach addresses growing concerns about data privacy in AI applications, particularly in industries handling sensitive information. By keeping data on-device, organizations can maintain compliance with privacy regulations while leveraging AI capabilities. This shift represents a fundamental reimagining of the AI privacy equation. Rather than treating privacy as a constraint that limits AI capabilities, on-device processing transforms privacy into a competitive advantage. Organizations no longer need to choose between powerful AI and data protection -- they can have both. The elimination of network dependencies also means that intermittent connectivity, traditionally a major limitation for AI applications, becomes irrelevant for core functionality. The approach is particularly valuable for sectors like healthcare and finance, where data sensitivity requirements often limit cloud AI adoption. Field applications such as equipment diagnostics and remote work scenarios also benefit from the offline capabilities. However, the shift to on-device processing introduces new security considerations that organizations must address. While the data itself becomes more secure by never leaving the device, the focus shifts to protecting the devices themselves and the AI models they contain. This creates new attack vectors and requires different security strategies than traditional cloud-based AI deployments. Organizations must now consider device fleet management, model integrity verification, and protection against adversarial attacks that could compromise local AI systems. Google's move comes amid intensifying competition in the mobile AI space. Apple's Neural Engine, embedded across iPhones, iPads, and Macs, already powers real-time language processing and computational photography on-device. Qualcomm's AI Engine, built into Snapdragon chips, drives voice recognition and smart assistants in Android smartphones, while Samsung uses embedded neural processing units in Galaxy devices. However, Google's approach differs significantly from competitors by focusing on platform infrastructure rather than proprietary features. Rather than competing directly on specific AI capabilities, Google is positioning itself as the foundation layer that enables all mobile AI applications. This strategy echoes successful platform plays from technology history, where controlling the infrastructure proves more valuable than controlling individual applications. The timing of this platform strategy is particularly shrewd. As mobile AI capabilities become commoditized, the real value shifts to whoever can provide the tools, frameworks, and distribution mechanisms that developers need. By open-sourcing the technology and making it widely available, Google ensures broad adoption while maintaining control over the underlying infrastructure that powers the entire ecosystem. The application currently faces several limitations that underscore its experimental nature. Performance varies significantly based on device hardware, with high-end devices like the Pixel 8 Pro handling larger models smoothly while mid-tier devices may experience higher latency. Testing revealed accuracy issues with some tasks. The app occasionally provided incorrect responses to specific questions, such as incorrectly identifying crew counts for fictional spacecraft or misidentifying comic book covers. Google acknowledges these limitations, with the AI itself stating during testing that it was "still under development and still learning." Installation remains cumbersome, requiring users to enable developer mode on Android devices and manually install the application via APK files. Users must also create Hugging Face accounts to download models, adding friction to the onboarding process. The hardware constraints highlight a fundamental challenge facing mobile AI: the tension between model sophistication and device limitations. Unlike cloud environments where computational resources can be scaled almost infinitely, mobile devices must balance AI performance against battery life, thermal management, and memory constraints. This forces developers to become experts in efficiency optimization rather than simply leveraging raw computational power. Google's Edge AI Gallery marks more than just another experimental app release. The company has fired the opening shot in what could become the biggest shift in artificial intelligence since cloud computing emerged two decades ago. While tech giants spent years constructing massive data centers to power AI services, Google now bets the future belongs to the billions of smartphones people already carry. The move goes beyond technical innovation. Google wants to fundamentally change how users relate to their personal data. Privacy breaches dominate headlines weekly, and regulators worldwide crack down on data collection practices. Google's shift toward local processing offers companies and consumers a clear alternative to the surveillance-based business model that has powered the internet for years. Google timed this strategy carefully. Companies struggle with AI governance rules while consumers grow increasingly wary about data privacy. Google positions itself as the foundation for a more distributed AI system rather than competing head-to-head with Apple's tightly integrated hardware or Qualcomm's specialized chips. The company builds the infrastructure layer that could run the next wave of AI applications across all devices. Current problems with the app -- difficult installation, occasional wrong answers, and varying performance across devices -- will likely disappear as Google refines the technology. The bigger question is whether Google can manage this transition while keeping its dominant position in the AI market. The Edge AI Gallery reveals Google's recognition that the centralized AI model it helped build may not last. Google open-sources its tools and makes on-device AI widely available because it believes controlling tomorrow's AI infrastructure matters more than owning today's data centers. If the strategy works, every smartphone becomes part of Google's distributed AI network. That possibility makes this quiet app launch far more important than its experimental label suggests.

Google's Latest App Lets Your Phone Run AI in Your Pocket -- Entirely Offline - Decrypt

Google has released a new app that nobody asked for, but everyone wants to try. The AI Edge Gallery, which launched quietly on May 31, puts artificial intelligence directly on your smartphone -- no cloud, no internet, and no sharing your data with Big Tech's servers. The experimental app -- released under the Apache 2.0 license, allowing anyone to use it for almost anything -- is available on GitHub, starting with the Android platform. The iOS version is coming soon. It runs models like Google's Gemma 3n entirely offline, processing everything from image analysis to code writing using nothing but your phone's hardware. And it's surprisingly good. The app, which appears to be aimed at developers for now, includes three main features: AI Chat for conversations, Ask Image for visual analysis, and Prompt Lab for single-turn tasks such as rewriting text. Users can download models from platforms like Hugging Face, although the selection remains limited to formats such as Gemma-3n-E2B and Qwen2.5-1.5 B. Reddit users immediately questioned the app's novelty, comparing it to existing solutions like PocketPal. Some raised security concerns, though the app's hosting on Google's official GitHub counters impersonation claims. No evidence of malware has surfaced yet. We tested the app on a Samsung Galaxy S24 Ultra, downloading both the largest and smallest Gemma 3 models available. Each AI model is a self-contained file that holds all its "knowledge" -- think of it as downloading a compressed snapshot of everything the model learned during training, rather than a giant database of facts like a local Wikipedia app. The largest Gemma 3 model available in-app is approximately 4.4 GB, while the smallest is around 554 MB. Once downloaded, no further data is required -- the model runs entirely on your device, answering questions and performing tasks using only what it learned before release. Even on low-speed CPU inference, the experience matched what GPT-3.5 delivered at launch: not blazing fast with the bigger models, but definitely usable. The smaller Gemma 3 1B model achieved speeds exceeding 20 tokens per second, providing a smooth experience with reliable accuracy under supervision. This matters when you're offline or handling sensitive data you'd rather not share with Google or OpenAI's training algorithms, which use your data by default unless you opt out. GPU inference on the smallest Gemma model delivered impressive prefill speeds over 105 tokens per second, while CPU inference managed 39 tokens per second. Token output -- how fast the model generates responses after thinking -- reached around 10 tokens per second on GPU on average and seven on CPU. The multimodal capabilities worked well in testing. Additionally, it appears that CPU inference on smaller models yields better results than GPU inference, although this may be anecdotal; however, this has been observed in various tests. For example, during a vision task, the model on CPU inference accurately guessed my age and my wife's in a test photo: late 30s for me, late 20s for her. The supposedly better GPU inference got my age wrong, guessing I was in my 20s (I'll take this "information" over the truth any day, though.) Google's models come with heavy censorship, but basic jailbreaks can be achieved with minimal effort. Unlike centralized services that ban users for circumvention attempts, local models don't report back about your prompts, so it can be a good practice to use jailbreak techniques without risking your subscription or asking the models for information that censored versions will not provide. Third-party model support is available, but it is somewhat limited. The app only accepts .task files, not the widely adopted .safetensor format that competitors like Ollama support. This significantly limits the available models, and although there are methods to convert .safetensor files into .task, it's not for everybody. Code handling works adequately, although specialized models like Codestral would handle programming tasks more effectively than Gemma 3. Again, there must be a .task version for it, but it can be a very effective alternative. For basic tasks, such as rephrasing, summarizing, and explaining concepts, the models excel without sending data to Samsung or Google's servers. So, there is no need for users to grant big tech access to their input, keyboard, or clipboard, as their own hardware is handling all the necessary work. The context window of 4096 tokens feels limited by 2025 standards, but matches what was the norm just two years ago. Conversations flow naturally within those constraints. And this may probably be the best way to define the experience. Considering you are running an AI model on a smartphone, this app will provide you a similar experience to what the early ChatGPT provided in terms of speed and text accuracy -- with some advantages like multimodality and code handling. But why would you want to run a slower, inferior version of your favorite AI on your phone, taking up a lot of storage and making things more complicated than simply typing ChatGPT.com? Privacy remains the killer feature. For example, healthcare workers handling patient data, journalists in the field, or anyone dealing with confidential information can now access AI capabilities without data leaving their device. "No internet required" means the technology works in remote areas or while traveling, with all responses generated solely from the model's existing knowledge at the time it was trained.. Cost savings add up quickly. Cloud AI services charge per use, while local models only require your phone's processing power. Small businesses and hobbyists can experiment without ongoing expenses. If you run a model locally, you can interact with it as much as you want without consuming quotas, credits, or subscriptions, and without incurring any payment. Latency improvements feel noticeable. No server round-trip means faster responses for real-time applications, such as chatbots or image analysis. It also means your chatbot won't ever go down. Overall, for basic tasks, this could be more than enough for any user, with the free versions of ChatGPT, Claude, Gemini, Meta, Reka, and Mistral providing a good backup when heavier computation is needed. Of course, this won't be a substitute for your favorite internet-connected chatbot anytime soon. There are some early adoption challenges. Battery drain concerns persist, especially with larger models; setup complexity might deter non-technical users; the model variety pales in comparison to cloud offerings, and Google's decision not to support .safetensor models (which account for almost 100% of all the LLMs found on the internet) is disappointing. However, Google's experimental release signals a shift in the philosophy of AI deployment. Instead of forcing users to choose between powerful AI and privacy, the company's offering both, even if the experience isn't quite there yet. The AI Edge Gallery delivers a surprisingly polished experience for an alpha release. Google's optimization demonstrates the creation of probably the best UI available for running AI models locally. Adding .safetensor support would unlock the vast ecosystem of existing models, transforming a good app into an essential tool for privacy-conscious AI users.

Offline AI just went mainstream with Google's new tool

Google released the Google AI Edge Gallery app last week, enabling users to download and run AI models from Hugging Face on their phones. The Android app, with an iOS version forthcoming, facilitates offline AI task execution. The Google AI Edge Gallery, labeled an "experimental Alpha release," is accessible for download via GitHub, accompanied by installation instructions. This app allows users to access various AI capabilities such as "Ask Image" and "AI Chat". Selection of a capability displays a list of appropriate models, including Google's Gemma 3n. The application allows users to locate, download, and run compatible AI models capable of generating images, answering questions, and writing/editing code. These models operate offline, utilizing the processing power of supported phones. The AI Edge Gallery also features a "Prompt Lab" for initiating single-turn tasks such as text summarization and rewriting. This lab provides task templates and adjustable settings for model customization. Can Google's tiny Gemma 3n AI really run smoothly on any device? Google cautions that performance may vary. Devices with more powerful hardware are expected to run models faster. Model size influences task completion time; larger models require more processing time. Google is soliciting feedback from the developer community regarding the Google AI Edge Gallery experience. The app operates under an Apache 2.0 license, permitting usage in most contexts, including commercial applications, without limitations.

This New Google App Can Run AI Models Locally On Your Device

Google's app will find AI models that are compatible to run on the device Google AI Edge Gallery, a new app for Android that allows users to experience running artificial intelligence (AI) models locally on a device, was released on Sunday. The Mountain View-based tech giant says the experimental app can find, download, and run large language models (LLMs) entirely on-device without requiring an Internet connection. Currently, only a limited number of use cases are available via the app, including chatting, image analysis, as well as a few text-based and coding tasks. The company said that an iOS version of the app will soon be released. The app is currently available as an "experimental alpha release" on the tech giant's GitHub listing. The company has provided an Android application package (APK) file that can be downloaded, as well as a detailed guide to installing it on an Android device. The app, which is 115MB in size, is available with a permissive Apache 2.0 license, which allows both academic and commercial use cases. Gadgets 360 staff members were able to download and install the app without any hassle. If you are new to installing APK files, Google has provided a detailed guide here. However, do note that downloading and installing APK files is always a risky move since these apps are not verified by a trusted app marketplace and might contain malware. Notably, we did not find any malware or viruses in this app. The Google AI Edge Gallery app comes with a list of AI models that users can download and run locally on their devices. This list of models will vary depending on how new the device is and whether it has an AI-enabled chipset or not. Additionally, the app also lets users import and run a model that is already downloaded on the device. There are three primary features users can explore with this app. First is Ask Image, which is an image analysis feature. After downloading and running a model, users will be able to upload an image and ask the AI questions about it. Second is AI Chat, which lets users have a conversation with the model. Since the app only supports local models, these models may not have up-to-date knowledge. Finally, the third feature is Prompt Lab, which is a space with several AI-powered features such as tone-based rewriting, text summarising, free-form generation, as well as code snippet generation. Notably, while running any AI model, users will be able to configure aspects such as tokens, temperature, accelerator, and more. Users can also check the benchmark metrics of the model.

Google lets Android phones run AI models without internet

Google has introduced the AI Edge Gallery app for Android, enabling users to run AI models from Hugging Face locally on their smartphones without an internet connection. This experimental release allows users to access tools like 'Ask Image' and 'AI Chat,' utilizing models such as Google's Gemma 3n.Different artificial intelligence (AI) models, which can create images, answer questions, or code, can now be run locally on Android smartphones. Last week, Google launched a new app that enables users to run various AI models from the Hugging Face platform directly on their phones, without an internet connection. The app, named Google AI Edge Gallery, is currently available for Android, and an iOS version is expected soon. It searches, downloads, and runs AI models on the phone, using its built-in processor. AI models that run online in the cloud are usually more powerful, but there could be certain drawbacks. For example, users may be uncomfortable sharing personal or sensitive data with remote servers, or they might want to use AI features when they don't have Wi-Fi or cellular data. Google is calling this an "experimental Alpha release". The app can be downloaded from GitHub. Google AI Edge Gallery's home screen shows shortcuts to different AI tools like "Ask Image" and "AI Chat." When you tap on one, you'll see a list of models that work for that task, including Google's own Gemma 3n model. The app also includes a feature called "Prompt Lab," where users can try out single-task prompts -- like summarising or rewriting text. It includes task templates and settings that let users customise how the models behave. The caveats related to AI operations remain. Google notes that how well the app works depends on the phone. Newer, more powerful phones will run the models faster. Also, larger models take longer to finish tasks, like answering questions about images, compared to smaller models. Google has invited feedback on the app from developers. It has been released under the Apache 2.0 license, which means it is available for public use , and even for commercial purposes.

Google Debuts App to Let Users Run AI Models on Their Phones | PYMNTS.com

The tool lets users find, download and run compatible models that do things like generating images, answering questions, or writing and editing code. The models run offline using phones' processors, without any need for an internet connection. The report noted that while AI models running in the cloud are in many cases more powerful than their local counterparts, they also have their drawbacks. Some users might not be comfortable with sending personal or sensitive data to a remote data center, or want to have models available without needing to seek out a Wi-Fi or cell connection. Google is cautioning that users' experience may vary when it comes to performance, the report said. Newer devices with more powerful hardware will invariably run models faster, but the model size also factors into performance. Larger models will take more time to carry out tasks than smaller ones. In other Google/AI news, PYMNTS wrote last week about the way the company's new AI Overviews and AI mode have changed the rules of search. So says David Hunter, CEO of SEO (search engine optimization) analytics company Local Falcon. "It is fundamentally changing the search engine optimization universe," Hunter said in an interview with PYMNTS. "It's not just a little algorithm update. ... The concept of being visible in a search engine is changing dramatically." In the past, businesses could optimize their content for Google's bots that crawl websites by inserting inbound links and populating pages with keywords. However, Local Falcon's research finds that Google's AI now analyzes queries differently, using large language models to come up with conversational results based on contextual understanding of user intent. The findings mark a "significant shift" in how Google determines which businesses show up and in what order, according to the company. It's no longer about stuffing keywords and hoping to produce traffic, Hunter said. Brands need to write good content that has conversational relevance and offer up proof that "you know what you're talking about."