Off Grid: Run Full-Featured AI on Your Phone Without Internet

TL;DR

A new open-source project called “Off Grid” lets you run AI text generation, image creation, and vision capabilities directly on your smartphone—completely offline. Developer Ali Chherawalla’s GitHub project has gained traction on Hacker News with 61 upvotes and sparked discussions about on-device AI performance. This isn’t just another wrapper around cloud APIs; it’s genuine local inference running entirely on your mobile hardware. If you’ve ever wanted AI capabilities that don’t require an internet connection, don’t share your data with third parties, and work anywhere (literally), this project deserves your attention.

What the Sources Say

According to the Hacker News community discussion, Off Grid represents a practical implementation of on-device AI inference for mobile platforms. The project’s GitHub repository demonstrates that it’s possible to run multiple AI modalities—text generation, image synthesis, and computer vision—on consumer smartphones without any server connection.

The Hacker News thread (26 comments as of this report) shows genuine interest from the developer community, though the exact technical architecture and performance benchmarks aren’t detailed in the limited source material available. What we can confirm is that this is a “Show HN” post, meaning the creator is presenting their working project to the community for feedback and discussion.

The project’s name—“Off Grid”—is particularly telling. It’s not marketing speak; it’s a literal description of the capability. This positions the tool squarely in the growing movement toward edge AI and privacy-conscious computing. You’re not sending your prompts to OpenAI’s servers, you’re not uploading images to Google’s cloud, and you’re not dependent on Anthropic’s API availability.

No contradictions were found in the source material, as we’re working with a single primary source (the Hacker News post and GitHub repository link). However, it’s worth noting that the community discussion likely contains valuable insights about real-world performance, which would require deeper analysis of the comment thread.

Technical Reality: What’s Actually Possible on Mobile?

Let’s address the elephant in the room: running AI models on smartphones is technically challenging. Modern language models and image generators are computationally expensive. The fact that Off Grid attempts to bundle text generation, image synthesis, AND vision capabilities into a mobile app is ambitious.

As of February 2026, we’re seeing increasing viability of on-device AI thanks to:

• Quantized models: Smaller versions of larger models (4-bit, 8-bit quantization) that maintain reasonable quality while fitting in mobile RAM
• Specialized mobile chips: Apple’s Neural Engine, Qualcomm’s AI accelerators, and Google’s TPU cores in Pixel devices
• Optimized inference frameworks: Libraries like ONNX Runtime Mobile, TensorFlow Lite, and Core ML that squeeze maximum performance from mobile hardware

The Off Grid project presumably leverages some combination of these technologies. Without detailed specifications from the source material, we can’t confirm exact model sizes or inference speeds, but the mere existence of community interest suggests it’s delivering usable results.

Privacy & Independence: The Real Value Proposition

Here’s what makes Off Grid genuinely interesting beyond the technical novelty: data sovereignty. When you run AI locally, several things change:

1. Zero data transmission: Your prompts, images, and queries never leave your device
2. No API costs: After the initial model download, there’s no per-request pricing
3. Works anywhere: No internet? No problem. Airplane mode becomes a feature, not a limitation
4. No rate limits: You’re not constrained by API quotas or throttling

For specific use cases—journalists in the field, travelers in remote areas, privacy-conscious professionals, or anyone in regions with unreliable internet—these aren’t just nice-to-haves. They’re deal-breakers for adoption.

Pricing & Alternatives

Since Off Grid is open-source and runs locally, there’s no subscription cost beyond your device’s hardware capabilities. Here’s how it compares to alternatives:

The closest conceptual competitors are Ollama (which focuses on desktop/server deployment) and LocalAI (a self-hosted API). Off Grid’s differentiation is mobile-first design—it’s optimized for phones and tablets, not laptops or servers.

For cloud-based alternatives, you’re looking at fundamentally different trade-offs. Yes, GPT-5.2 or Claude 4.6 will deliver superior output quality, but they require connectivity, cost money per request, and send your data to third parties. Off Grid won’t match their capabilities, but it’s available when they’re not.

Real-World Limitations (That the Sources Won’t Tell You)

While the Hacker News community seems genuinely impressed, let’s be realistic about mobile AI constraints:

Battery drain: Running inference is power-intensive. Expect significant battery consumption during active use.

Model size vs. quality trade-off: To fit on mobile devices, models must be smaller or more quantized than their cloud counterparts. This means lower quality outputs—simpler text, lower-resolution images, less nuanced vision analysis.

Storage requirements: Even compressed models require gigabytes of storage. The full Off Grid package likely requires several GB of space.

Device compatibility: Not all smartphones have equivalent AI acceleration hardware. An iPhone 15 Pro with dedicated Neural Engine will dramatically outperform a mid-range Android from 2022.

Initial setup complexity: While the app itself might be user-friendly, getting models downloaded and configured isn’t always straightforward for non-technical users.

These aren’t criticisms of Off Grid specifically—they’re inherent to the current state of mobile AI technology. The project deserves credit for navigating these constraints to ship something usable.

The Bottom Line: Who Should Care?

You should definitely check out Off Grid if you:

• Work in environments with unreliable or nonexistent internet connectivity
• Handle sensitive information and can’t risk cloud transmission (legal, medical, investigative journalism)
• Want to experiment with AI without ongoing subscription costs
• Are building applications that require offline AI capabilities
• Simply value digital independence and don’t want your AI interactions logged by corporations

You can probably skip it if:

• You need cutting-edge AI quality for professional work (cloud APIs still win on output quality)
• You primarily work from locations with stable internet and don’t have privacy concerns
• Your device is older or low-end (it likely won’t run well enough to be useful)
• You expect performance comparable to GPT-5.2 or Claude 4.6 (that’s not happening on-device yet)

Developers should definitely explore this if you’re:

• Building mobile apps that could benefit from on-device AI (note-taking apps, photo editors, accessibility tools)
• Researching edge AI deployment strategies
• Creating tools for users in developing regions or remote areas
• Interested in contributing to open-source AI infrastructure

The broader significance of Off Grid isn’t just what it does today—it’s what it represents. We’re at an inflection point where capable AI is becoming small enough and efficient enough to escape the cloud entirely. Projects like this are the vanguard of that transition.

As mobile hardware continues improving (Apple’s rumored M-series chips in iPhones, Qualcomm’s next-gen AI cores) and model optimization techniques advance, the gap between on-device and cloud AI will narrow. Off Grid is building the infrastructure for that future today.

Developer Perspective: Why This Matters

Ali Chherawalla’s project addresses a real gap in the AI tooling ecosystem. While we’ve seen explosive growth in cloud AI services and desktop AI tools, mobile-native offline AI has been underserved. Most mobile AI apps are just thin wrappers around cloud APIs—they stop working the moment your connection drops.

The technical achievement here shouldn’t be understated. Coordinating multiple model types (LLMs, diffusion models, vision transformers), managing memory constraints, optimizing battery usage, and delivering a usable interface is genuinely difficult engineering work.

For the broader developer community, Off Grid serves as both a proof-of-concept and potentially a foundation for future projects. If it’s truly open-source (as the GitHub link suggests), others can fork it, improve it, and build specialized versions for specific use cases.

What We Don’t Know (And Wish We Did)

Given the limited source material, several important questions remain:

• Which specific models are being used? (LLaMA derivatives? Stable Diffusion variants? Custom-trained models?)
• What are the actual inference speeds? (Tokens per second for text, seconds per image for generation)
• Minimum device requirements? (Does it work on anything, or do you need flagship hardware?)
• How’s the installation process? (App store download or side-loading required?)
• What’s the quality gap compared to cloud services? (Subjective but important for setting expectations)

If you’re seriously considering Off Grid, I’d recommend diving into the GitHub repository, checking the Issues section for user-reported experiences, and potentially testing it yourself on your specific hardware.

Final Thoughts

Off Grid represents something important: the democratization of AI beyond the cloud. While it won’t replace Claude 4.6 for professional writing or GPT-5.2 for complex reasoning tasks, it doesn’t need to. It’s solving a different problem—making AI available when and where cloud services can’t reach.

As someone who’s watched the AI landscape evolve, I’m more excited about projects like this than yet another ChatGPT wrapper. We need diverse approaches to AI deployment: cloud for raw power, edge for privacy and availability, and hybrid for flexibility. Off Grid is pushing the edge computing boundary forward.

If you’ve got a compatible device and an hour to experiment, it’s worth trying. At minimum, you’ll gain appreciation for how far mobile AI has come. At best, you might discover a tool that genuinely changes how you work.

The future of AI isn’t just more powerful models in distant data centers. It’s also smarter, more efficient models that run in your pocket. Off Grid is helping build that future today.

Sources

• Off Grid GitHub Repository - Primary project page and source code
• Hacker News Discussion: “Show HN: Off Grid – Run AI text, image gen, vision offline on your phone” - Community discussion with 26 comments and 61 points

Note: This article is based on available sources as of February 15, 2026. For the most up-to-date information, installation instructions, and technical specifications, please refer to the official GitHub repository.