AI Without Sending Data to Cloud: Local and Offline Guide

How AI Runs Without Cloud Processing

You can run Artificial Intelligence (AI) without sending data to cloud servers by using hardware-isolated on-device models, activating vendor offline toggles, or running open-weight software directly on your own computer. At Ban the Bots, we audit vendor privacy claims to identify which features stay on your device and which ones quietly upload your personal files. If you want to use machine learning without exposing your documents, notes, or dictation to third-party data centers, you must understand where your operating system draws the line between local chips and remote infrastructure.

Vendors use three words that blur together: on-device, private cloud, and offline. Understanding the boundaries between these tiers prevents accidental data leakage when you interact with modern software.

On-device execution means the mathematical weights of a Large Language Model (LLM) sit in local storage and run inside your device memory. The calculations happen on your Central Processing Unit (CPU), Graphics Processing Unit (GPU), or Neural Processing Unit (NPU). No data packets containing your prompts or generated responses leave the hardware network interface.

Private cloud computation represents an intermediary model where requests leave your physical device but travel to isolated, secured server environments. These environments promise stateless processing, meaning the server does not store or inspect your data after completing the request. However, this model still requires network transmission, introducing reliance on corporate security architectures rather than absolute physical isolation.

Offline execution refers to software that functions with the network connection severed. An application operating offline delivers verifiable privacy because physical disconnection guarantees zero outbound traffic. Mainstream smartphone and computer operating systems rarely use a single pure approach by default. Most consumer systems deploy hybrid configurations that shift between local hardware and remote data centers depending on what the feature needs. To keep your information private, you must adjust the vendor settings below or consult our recommendations on how to protect your privacy online.

Apple Intelligence and Cloud Routing

Apple routes consumer requests between local hardware models and remote servers depending on the computational size of the prompt. Apple says that in many cases the models run entirely on your Mac, iPhone, or iPad.

Official Apple documentation states: “In many cases, Apple Intelligence models run entirely on device so that a task can be completed without data leaving your device.” When a user initiates an action requiring computational capacity beyond local silicon capabilities, such as generating novel imagery via Image Playground, Apple routes the request to Private Cloud Compute (PCC).

Apple announced Private Cloud Compute on June 10, 2024, describing it as “a groundbreaking cloud intelligence system designed specifically for private AI processing.” Apple built this architecture because “to process more sophisticated requests, Apple Intelligence needs to be able to enlist help from larger, more complex models in the cloud.” PCC servers run on custom Apple silicon equipped with Secure Enclave and Secure Boot technologies. Apple asserts that data sent to PCC “is not stored or made accessible to Apple” and is “not retained by Private Cloud Compute.” The system uses stateless computation alongside an independently verifiable transparency log that allows security researchers to inspect running code.

You can audit these interactions directly on your device. Navigate to Settings > Privacy & Security > “Apple Intelligence & PCC Report” to review a technical log of requests processed locally versus those dispatched to Private Cloud Compute.

Third-party integrations require separate user authorization. OpenAI powers an optional extension within Siri. On macOS, navigate to System Settings > Siri > ChatGPT, where you can turn off Use ChatGPT completely. Apple notes: “You control when ChatGPT is used and are asked before any of your information is shared.” The “Confirm Requests” toggle makes that prompt appear each time. Apple also says your Internet Protocol (IP) address is obscured from ChatGPT, and requests submitted without an account stipulate that OpenAI “must not use your request to improve or train its models.” If you do not wish to use Apple Intelligence at all, follow our walkthrough on how to turn off Apple Intelligence.

Google Android and Gemini Nano on Devices

Google provides on-device machine learning through Gemini Nano, which processes specific smartphone tasks without an active internet connection. This architecture isolates sensitive personal data on the phone itself.

In an engineering update on August 27, 2024, Google highlighted the operational benefits: “Having the LLM on-device is beneficial ... more privacy, less latency, and it works wherever they need since there's no internet required.” On Google Pixel hardware, Gemini Nano powers tasks like automated summaries inside the Google Recorder application. Google deployed the original Gemini Nano model to Pixel 8 devices, while deploying Gemini Nano with multimodality to Pixel 9 devices to handle audio, text, and visual inputs locally.

On desktop computers, Chrome includes a built-in Gemini Nano model designed to execute AI tasks directly within the browser client. According to official developer documentation, “No data is sent to Google or any third party when using the model” once the required software components are installed. However, enabling this local model requires an initial download that consumes approximately 22 Gigabytes (GB) of disk storage. If you want to eliminate Google machine learning modules from your browser, read our guide on how to turn off Gemini in Chrome, or consider transitioning to degoogled phones for mobile operating systems that ship without Google services.

Microsoft Copilot+ PCs and Windows Recall

Microsoft confines select Windows AI features to hardware containing a dedicated Neural Processing Unit (NPU). This hardware requirement separates local background processing from cloud-based Copilot services.

Microsoft defines a Copilot+ Personal Computer (PC) as an endpoint “powered by a high-performance Neural Processing Unit (NPU) ... that can perform more than 40 trillion operations per second (TOPS).” Compatible silicon architectures include Qualcomm Snapdragon X, AMD Ryzen AI 300, and Intel Core Ultra 200V processors.

The most scrutinized local feature on Windows is Recall. Running Recall requires a “Copilot+ PC that meets the Secured-core standard”, a 40 TOPS NPU, 16 GB of Random Access Memory (RAM), and at least 256 GB of storage. Microsoft states: “By default, saving snapshots for Recall aren't enabled. You need to opt in.” When enabled, photographic snapshots of desktop activity are “saved and encrypted to your local hard drive.” The capability remains labeled “(preview)”.

On April 25, 2025, Microsoft announced the “general availability (GA) of exclusive AI experiences like Recall (preview)” on retail Copilot+ devices. Microsoft confirmed: “Recall data is processed locally on your device, meaning it is not sent to the cloud.” The company scheduled availability for users in the European Economic Area (EEA) for “later in 2025.”

You can manage or disable Recall on compatible devices. To pause or halt capture, navigate to Settings > Privacy & security > Recall & snapshots, and toggle Save snapshots to Off. You can permanently uninstall the software through the “Turn Windows features on or off” management menu. Microsoft documentation notes that “Microsoft can't access or view the snapshots” and requires biometric authentication via Windows Hello to decrypt local logs. For complete removal instructions regarding Microsoft assistant software, see our resource on how to turn off Copilot in Windows.

Samsung Galaxy AI On-Device Settings

Samsung allows smartphone owners to restrict artificial intelligence computations to local hardware by flipping a dedicated privacy toggle. Samsung documents which features keep working with the switch on and which stop.

To enforce local processing on a Galaxy device: Navigate to Settings, then tap Galaxy AI, and then tap the switch by Process data only on device. Once toggled, the Galaxy AI features that need Samsung's cloud stop working rather than uploading.

Activating this restriction alters the software capabilities available on the phone. Samsung documentation establishes the functional trade-offs:

• Features that remain functional on device: Language translation tools, ambient wallpapers, and Google Messages Magic Compose operate entirely on local silicon.
• Features that stop working without cloud access: “Auto formatting, auto summarizing, auto corrections, and image editing” cease functioning until you disable the restriction.

This technical boundary illustrates the central trade-off of consumer AI. Samsung runs image editing and auto summarizing in the cloud rather than on the phone. Activating the on-device toggle preserves data privacy at the direct expense of generative editing tools. If you prefer to disable Samsung automation entirely, consult our guide on how to turn off Galaxy AI.

Running Local Models on Your Own Computer

Running open-weight Large Language Models (LLMs) on your personal desktop provides complete data isolation from external cloud infrastructure. By downloading model weight files directly to your hard drive, you execute queries without transmitting text over an active network interface.

Several software applications package open-weight models into consumer-friendly interfaces:

• Ollama: A command-line utility compatible with macOS, Windows, Linux, and Docker environments, distributed under the open-source Massachusetts Institute of Technology (MIT) license. Ollama outlines the following system memory thresholds: “You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.” Ollama publishes no formal telemetry policy in its primary documentation; network access is required to download model weight files initially, but runtime execution runs locally.
• LM Studio: A graphical application running on Apple Silicon Macs, x64/ARM64 Windows PCs, and x64 Linux PCs. The developers confirm the application “can operate entirely offline.” While LM Studio has always been free for personal use, the company announced that as of July 8, 2025, it is “free to use both at home and at work.” In its official application privacy statement, LM Studio confirms that “the application does not include telemetry or user-specific tracking” and “none of your messages, chat histories, and documents are ever transmitted from your system.” Network connections occur exclusively to search for models, download weights, or check for software updates, sending only basic operational data like app version, Operating System (OS), and IP address.
• GPT4All: Built by Nomic AI, this open-source ecosystem “runs large language models (LLMs) privately on everyday desktops & laptops” across Windows, macOS 12.6+, and Ubuntu. The software emphasizes accessibility by operating with “No API calls or GPUs required”, and it ships a local document indexing tool called LocalDocs.
• Jan: An open-source desktop client licensed under Apache 2.0 that allows you to “Download and run LLMs with full control and privacy.” Jan supports macOS 13.6+, Windows 10+, and Linux, running local models like Llama, Gemma, Qwen, and GPT-oss. Users must note that Jan also offers cloud integrations for OpenAI, Anthropic, Mistral, and Groq. If your objective is zero cloud leakage, you must avoid configuring these optional external Application Programming Interface (API) keys.
• llamafile: Originally initiated as a Mozilla Builders project and now maintained by Mozilla.ai under the Apache 2.0 license, llamafile “lets you distribute and run LLMs with a single file.” It combines llama.cpp with Cosmopolitan Libc to turn complex model weights into standalone executable binaries. On Windows systems, executables are subject to an operating system limit restricting single-file binaries to under 4 GB.

When selecting among these local engines, choose LM Studio if you want a graphical app with a published no-telemetry statement, or GPT4All if you want a graphical app that runs without a GPU. Choose Ollama or llamafile if you need lightweight command-line tools or single-binary deployments for scripting and local servers.

What Local Models Do Not Solve

Executing an AI model locally prevents network data exfiltration but does not resolve structural flaws inherent to machine learning models. Isolating calculations to local silicon addresses data transit, but it leaves several fundamental issues intact.

Local execution does not change how the model was trained. The weights on your computer were built from other people's text, and running them offline keeps your prompts private without changing that.

Applications that run models locally still require network interfaces for installation and software updates. When you download model weights or check an application repository, your device transmits metadata, including your IP address, operating system version, and client build numbers. If an application contains closed-source components or third-party cloud toggles, an accidental click or unexpected update can re-enable outbound connections. Complete network isolation requires placing your computer behind a strict firewall or severing internet access entirely.

A local model is still an LLM, so it can state wrong facts and write broken code with full confidence. Running it offline protects your data and does nothing for its accuracy, so check its output the same way you would check a cloud model's.

Smaller Local Machine Learning in Firefox

Firefox embeds small, task-specific machine learning models directly within the browser engine to perform translation without third-party network requests.

Mozilla explains the mechanics of its translation engine: “translations are done locally” and “no data from your chosen device leaves your device or relies on cloud services for translation.” Because all language models execute within the client, “Mozilla doesn't know what web page you translate.” This keeps your browsing habits private from translation servers.

If you prefer to disable machine learning features entirely, Mozilla added unified configuration toggles. On February 24, 2026, Mozilla introduced Firefox AI Controls. By navigating to Settings > “AI Controls”, users can activate the master switch labeled “Block AI enhancements”. This control halts both current and future automated features across the browser, including local website translations, image alternative text generation in Portable Document Format (PDF) files, automated tab groupings, link previews, and sidebar assistant interfaces. Users can also manage each feature individually if they wish to retain local translations while blocking other tools. For broader comparisons of minimal software footprints, explore our analysis of browsers without AI.

Who an Offline AI Workflow Does Not Suit

An entirely offline AI workflow does not suit users who lack dedicated modern hardware or who require frontier reasoning capabilities. Running parameter weights locally demands substantial physical computing resources.

Ollama's own guidance is 8 GB of RAM for 7B models, 16 GB for 13B, and 32 GB for 33B. If your computer sits under the lowest of those, do not plan on a local model; use the vendor on-device switches above instead, or skip generative tools altogether.

Furthermore, an offline workflow does not serve users who require live web information, real-time sports results, financial market tracking, or automated third-party app integrations. A local model knows nothing after its training cutoff. If your daily workflow depends on real-time data lookups, local offline AI will not meet your requirements.

What Would Change Our Verdict

Our recommendation to use dedicated local tools over mainstream consumer assistants would change if hardware manufacturers eliminate transparent offline toggles. If operating system vendors make cloud routing mandatory or remove audit logs, consumer trust in local isolation will collapse.

Specifically, if Apple removes the “Apple Intelligence & PCC Report” auditing tool, or if Samsung eliminates the “Process data only on device” toggle in future software updates, we would advise users to treat those operating systems as fully public cloud environments. Similarly, if independent network packet analysis reveals that background diagnostic telemetry leaks prompt fragments despite local settings, we would cease recommending built-in manufacturer switches.

We would also alter our position on independent runtimes like LM Studio or Ollama if their developers introduce mandatory cloud account logins, background usage telemetry, or closed-source remote dependencies. As long as these tools provide verifiable offline operation without user tracking, running local models remains the most secure method to evaluate AI capabilities without sacrificing data privacy.

Audit your phone and desktop settings today by enabling the local processing switches on your operating system, or download an open-weight tool like LM Studio or Ollama to test AI without sending data to cloud servers.

Frequently Asked Questions

Can AI run without the internet?

Yes, AI models can run entirely without an internet connection if the model weights and execution runtime are installed directly on your device. Open-weight tools like Ollama, LM Studio, and llamafile execute on local processors without initiating network requests once the initial software download is complete.

Does Apple Intelligence send my data to the cloud?

Apple Intelligence handles many routine tasks on device, but it routes more complex requests to Private Cloud Compute (PCC) servers. Apple states that PCC servers process data statelessly without retention, and you can audit which tasks left your hardware by reviewing the Apple Intelligence & PCC Report in your device settings.

Does Windows Recall upload my screenshots?

Microsoft states that Windows Recall does not upload your screenshots to the cloud. The feature runs exclusively on Copilot+ PCs with a 40 TOPS NPU, and snapshots are encrypted and stored locally on your hard drive. Recall is opt-in, labeled as a preview, and can be disabled or uninstalled entirely in Windows settings.

What is the easiest way to run AI locally?

The easiest way to run AI locally is to install a dedicated desktop application such as LM Studio or GPT4All. These programs provide a graphical user interface, manage model downloads automatically, and run completely offline without requiring technical configuration or command-line scripting.

How much RAM do I need to run a local AI model?

You need at least 8 GB of RAM to run small 7-billion parameter models effectively, 16 GB of RAM to run medium 13-billion parameter models, and 32 GB of RAM to execute larger 33-billion parameter models. Those figures come from Ollama's readme.

Sources

• Apple Intelligence & Privacy Policy (Apple documentation on on-device execution and Private Cloud Compute).
• Private Cloud Compute Security Blog (Technical architecture, stateless processing, and Apple silicon server specifications).
• Apple Support macOS Guide on ChatGPT Integration (Configuration steps, privacy controls, and data sharing toggles).
• Android Developers Blog on Gemini Nano (Pixel on-device LLM implementation, latency reductions, and offline capabilities).
• Chrome for Developers Built-in AI Guide (Local Gemini Nano browser integration and 22 GB storage footprint).
• Microsoft Learn NPU Hardware Specifications (Copilot+ PC requirements, 40 TOPS NPU threshold, and processor architectures).
• Microsoft Support on Retracing Steps with Recall (Hardware prerequisites, encryption standards, and preview status).
• Microsoft Support on Privacy and Control Over Recall (Snapshot management, biometric authentication, and removal steps).
• Windows Experience Blog on Copilot+ PC General Availability (April 2025 release details, local processing guarantees, and EEA rollout plans).
• Samsung Support on Galaxy AI Processing Settings (Step-by-step menu navigation and feature availability trade-offs).
• Ollama Repository Documentation (Platform compatibility and RAM specifications for 7B, 13B, and 33B models).
• Ollama MIT License File (Open-source terms and conditions).
• LM Studio Application Documentation (Hardware support and offline operational guarantees).
• LM Studio Announcement on Licensing (July 8, 2025 terms permitting free personal and workplace use).
• LM Studio Application Privacy Policy (Zero telemetry statement and data transmission specifications).
• GPT4All by Nomic AI Repository (Local desktop LLM execution without GPUs or API calls).
• Jan Desktop Assistant Repository (Apache 2.0 open-source architecture, local model support, and cloud API options).
• llamafile Repository (Mozilla.ai single-file binary distribution and 4 GB Windows limits).
• Firefox Local Website Translation Features (Client-side machine learning and zero tracking guarantees).
• Mozilla Blog on Firefox AI Controls (February 24, 2026 update introducing settings to block AI enhancements).