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Private LLM Review (2026): On-Device AI Chat for iPhone, iPad, and Mac

Β·9 min readΒ·By Hans Kuepper Β· Founder of PromptQuorum, multi-model AI dispatch tool Β· PromptQuorum
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Private LLM is a $4.99 one-time-purchase app for iPhone, iPad, and Mac that runs 140+ open-source AI models entirely on-device, with no account and no subscription. Made by Numen Technologies Limited, it uses OmniQuant and GPTQ quantization, which the developer says preserves more output quality per bit than the round-to-nearest quantization used in some competing apps. One purchase unlocks the full model library on all three Apple platforms and, via Family Sharing, up to six people. Readers who want a free alternative, or who use Android/Windows/Linux, should compare it with PocketPal AI, which is free and open source.

Private LLM, made by Numen Technologies Limited, is a paid, one-time-purchase app for iPhone, iPad, and Mac that runs open-source language models entirely on-device, with no account, no cloud fallback, and no subscription. It costs $4.99 on the Apple App Store β€” a single purchase that covers all three Apple platforms and, via Family Sharing, up to six people. The app supports more than 140 open-source models from families including Llama, Qwen, Gemma, Mistral, and Phi-4, and uses a quantization method called OmniQuant (paired with GPTQ) that the developer says preserves more model quality than the simpler round-to-nearest quantization used by some competing apps. The practical question for a reader comparing local-AI apps is not whether on-device chat works on an iPhone β€” several apps on this site already prove that β€” it is whether a one-time $4.99 purchase with 140+ curated models is worth it next to free alternatives like PocketPal AI or Enclave AI.

Private LLM Review (2026): On-Device AI Chat for iPhone, iPad, and Mac

Key Takeaways

  • Price: $4.99 one-time purchase on the Apple App Store; no subscription and no in-app purchases listed as of this review.
  • Platforms: iPhone, iPad, and Mac only β€” no Android, Windows, or Linux app on the App Store.
  • Developer: Numen Technologies Limited, a small, bootstrapped, EU-based team, per the developer's own site copy.
  • Model library: more than 140 open-source models, including Llama, Qwen, Gemma, Mistral, Phi-4, and DeepSeek R1 Distill-based models.
  • Quantization: uses OmniQuant and GPTQ, which the developer says produce better output quality per bit than the round-to-nearest (RTN) quantization used in some competing apps.
  • Privacy: the App Store privacy label states the developer collects no data from the app; no account or sign-in is required to chat.
  • Extras: Siri and Shortcuts integration via two App Intents, plus Family Sharing for up to six people on one purchase.
  • Version 1.9.15 (July 2026) is the current release; the app first launched on the App Store in June 2023.

πŸ“ In One Sentence

Private LLM is a $4.99 one-time-purchase iPhone, iPad, and Mac app by Numen Technologies Limited that runs 140+ open-source AI models entirely on-device using OmniQuant and GPTQ quantization, with no account, no cloud fallback, and no subscription.

πŸ’¬ In Plain Terms

Think of it as buying a local AI chat app once, the way you would buy a regular App Store app, instead of subscribing to a cloud chatbot β€” the model runs on your own phone or Mac, so nothing you type leaves the device.

What Private LLM Is

Private LLM is a native Apple app that downloads and runs open-source language models directly on an iPhone, iPad, or Mac, with no server-side component. Once a model is downloaded, the app needs no internet connection to generate a response β€” everything runs locally using the device's own CPU, GPU, and Neural Engine.

It is built and maintained by Numen Technologies Limited, which describes itself on its own site as "built by two engineers, not VCs" β€” a small, bootstrapped team rather than a venture-backed company. The app first appeared on the App Store in June 2023 as Private LLM - Local AI Chat (App Store ID 6448106860), and has been updated continuously since β€” the current release, version 1.9.15, shipped in July 2026.

Unlike apps that dispatch chat requests to a remote API, Private LLM's entire value proposition rests on local inference: the developer's marketing copy states "no cloud, no tracking, no logins" and "every conversation stays on-device." This review evaluates that claim against what the App Store privacy label and the developer's own FAQ document, rather than taking the tagline at face value.

How to Get Started

Setting up Private LLM takes four steps and no account creation. The whole process, from App Store download to a first response, typically takes a few minutes plus however long the chosen model takes to download.

  1. 1
    Buy and install the app
    Why it matters: Download [Private LLM from the Apple App Store](https://apps.apple.com/us/app/private-llm-local-ai-chat/id6448106860) for $4.99. This is a one-time purchase β€” there is no free trial tier and no recurring subscription to manage.
  2. 2
    Pick a model that fits your device
    Why it matters: Open the in-app model browser and choose a model sized for your hardware. The developer's own guidance suggests most iPhones run Llama 3.2 3B or Qwen3 4B comfortably, iPhone 15 Pro and newer can handle Llama 3.1 8B, and a Mac with 48 GB of unified memory can run Llama 3.3 70B.
  3. 3
    Download the model
    Why it matters: Model files range from roughly 2 GB to tens of gigabytes depending on parameter count and quantization level. This step requires an internet connection; every step after it does not.
  4. 4
    Chat entirely offline
    Why it matters: Once the model is downloaded, turn on airplane mode if you want to verify the offline claim yourself β€” chat, summarization, and rephrasing (on Mac) all run without a network connection.
  5. 5
    Optional: connect Siri and Shortcuts
    Why it matters: Private LLM exposes two App Intents for Siri and the Shortcuts app, letting you trigger a model response from a voice command or an automation without opening the app directly.

Pricing: One-Time Purchase Explained

Private LLM costs $4.99 as a single, one-time purchase on the Apple App Store β€” there is no subscription and no in-app purchases listed on the current listing. That price was verified directly against the App Store listing for this review.

$4.99 (one-time)

What it covers:
Full app on iPhone, iPad, and Mac; access to the full 140+ model library; Family Sharing for up to six people
What it does not include:
Any Android, Windows, or Linux version β€” the App Store purchase does not unlock a cross-platform license

App Store prices can change without notice and may differ by region. Confirm the current price on the App Store listing before purchasing. Verified for this review on 2026-09-05.

Supported Models and OmniQuant Quantization

Private LLM's library includes more than 140 open-source models, spanning general-purpose, coding, and language-specific fine-tunes. Named families in the developer's own documentation include Llama 3, 3.1, 3.2, and 3.3; Qwen 2.5 and Qwen3-based models; Gemma 2 and Gemma 3; Phi-4; Mixtral; and DeepSeek R1 Distill-based models, alongside region-specific options such as SauerkrautLM (German), DictaLM (Hebrew), RakutenAI (Japanese), and Yi (Chinese).

The app quantizes these models using OmniQuant, paired with GPTQ for some models β€” both are optimization-based quantization methods rather than the simpler round-to-nearest (RTN) approach some competing local-AI apps use. According to the developer's own comparison pages, optimization-based quantization tunes the quantization range against calibration data, which can preserve more of the original model's output quality at a given bit-width than RTN. This is the developer's own technical claim, sourced from their documentation β€” it has not been independently benchmarked by PromptQuorum against Private LLM's specific quantized model files.

Hardware guidance from the developer: most iPhones run Llama 3.2 3B or Qwen3 4B comfortably; iPhone 15 Pro and newer can run Llama 3.1 8B; and a Mac with 48 GB of unified memory can run Llama 3.3 70B. These are the developer's own recommendations, not independently benchmarked figures β€” actual performance depends on quantization level, context length, and background app load.

Platforms: iPhone, iPad, Mac, and Vision Pro

iPhone

What to expect:
Requires iOS 17.0 or later and an A12 Bionic chip or newer (iPhone XS and later, per the App Store listing). Model size choice should match RAM.
Important note:
Most iPhones handle 3B–4B parameter models well; only iPhone 15 Pro and newer are recommended for 8B models, per the developer.

iPad

What to expect:
Runs the same app and model library as iPhone. The developer's FAQ recommends 4 GB RAM minimum, with an iPad Pro (16 GB) recommended for larger models.
Important note:
Older, lower-RAM iPads are limited to smaller quantized models.

Mac

What to expect:
Native app for Apple Silicon Macs (M-series). Also includes macOS-specific writing services (grammar correction, summarization, rephrasing) that other apps can call into.
Important note:
The developer's FAQ states Intel Macs are technically supported but not recommended β€” inference is noticeably slower without Apple Silicon's unified memory and Neural Engine.

Apple Vision Pro

What to expect:
The App Store listing shows Vision Pro compatibility for the same app.
Important note:
This review did not independently test the Vision Pro experience; treat it as App Store-listed compatibility, not a hands-on verified feature.

Android, Windows, Linux

What to expect:
No official listing on Google Play, the Microsoft Store, or any Linux package repository.
Important note:
An unofficial beta APK has circulated outside the Google Play Store at points in the app's history; it is not part of the developer's primary marketing site or supported release channel, so this review does not treat Android as a supported platform.

Privacy: What Private LLM Does and Does Not Collect

Private LLM's App Store privacy label states the developer does not collect any data from the app, and the app requires no account, login, or sign-up to use. The developer's own marketing describes the product with the phrases "no cloud, no tracking, no logins" and states that conversations "never leave the device."

Because inference runs locally after a model is downloaded, there is no chat data to transmit to a server during normal use β€” the architecture itself, not just a policy promise, is what keeps conversations on-device.

  • No account required. You can download, purchase, and use the app without creating a profile or signing in.
  • No data collection, per the App Store label. Apple's privacy nutrition label for this listing shows no data collected from the app.
  • iCloud sync of chat history is not documented. The developer's public FAQ does not describe iCloud sync of conversations across devices β€” this review treats that as unconfirmed rather than assuming it exists. If cross-device chat sync matters to you, verify current behavior directly in the app before relying on it.
  • Sandboxed execution. The app runs within Apple's standard app sandbox, the same isolation every App Store app is subject to β€” this is an Apple platform guarantee, not a Private LLM-specific feature.

Company History and Version Milestones

Private LLM launched on the App Store in June 2023, with version 1.0.1 for iOS and version 1.0.2 for macOS both shipping on June 2, 2023. It is developed by Numen Technologies Limited, which describes itself as a small, bootstrapped team without venture-capital funding.

  • June 2023. Initial App Store release (iOS 1.0.1, macOS 1.0.2) with a fine-tuned baseline model.
  • July 2023. Siri and Shortcuts support (App Intents) added.
  • September 2023. Compatibility added for the iPhone 15 series.
  • December 2023. Support extended to older iPhones and iPads with as little as 3 GB of RAM.
  • January 2024. Multi-model downloading introduced, expanding the library to include TinyLlama, StableLM, Phi-2, Mistral, Llama, and Gemma family models.
  • February 2024. macOS-specific writing services added: grammar correction, summarization, and rephrasing that other Mac apps can call into.
  • March 2024. Model switching without leaving the active chat interface.
  • July 2026. Version 1.9.15 moved model downloads to a CDN instead of Hugging Face, which the release notes describe as faster; this is the current version as of this review.

Trade-Offs: Benefits vs. Limitations

One-time $4.99 purchase

What it means in real use:
No subscription to track or cancel; pay once, use indefinitely across your Apple devices.
Limitation / caveat:
It is not free β€” readers who want a no-cost option should compare it with PocketPal AI or Enclave AI.

140+ model library

What it means in real use:
Wide choice of general-purpose, coding, and language-specific models without hunting for GGUF files yourself.
Limitation / caveat:
The library is curated by the developer; you cannot import an arbitrary custom fine-tune the way some open-source apps allow.

OmniQuant and GPTQ quantization

What it means in real use:
The developer states this preserves more model quality per bit than simpler round-to-nearest quantization.
Limitation / caveat:
This is the developer's own technical claim; PromptQuorum has not independently benchmarked Private LLM's specific model files against RTN-quantized equivalents.

No account, no data collection

What it means in real use:
Use the app immediately after purchase with nothing to sign up for; the App Store privacy label shows no data collected.
Limitation / caveat:
Because the app is closed-source, the no-collection claim cannot be independently code-audited the way an open-source app's can.

Siri and Shortcuts integration

What it means in real use:
Trigger model responses from voice commands or automations without opening the app.
Limitation / caveat:
iOS restricts background GPU access, so Shortcuts-triggered generation may be slower or more limited than foreground chat.

Family Sharing for up to six people

What it means in real use:
One $4.99 purchase can cover an entire Apple Family Sharing group.
Limitation / caveat:
Each family member still needs their own supported device and enough storage/RAM for the models they choose.

Private LLM vs. Alternatives

Private LLM

Platforms:
iPhone/iPad/Mac (Apple only)
Price:
$4.99 one-time purchase
Model flexibility:
140+ curated models; OmniQuant/GPTQ quantization
Key difference:
Paid, closed-source, curated library β€” no arbitrary GGUF import
Articles about Private LLM (10)

+4 more not shown

PocketPal AI

Platforms:
iPhone/iPad, with some Android support
Price:
Free, open source
Model flexibility:
Any GGUF file the user sources from Hugging Face or elsewhere
Key difference:
Free and open-source; requires more manual model management

Enclave AI

Platforms:
Varies by release β€” check current listing
Price:
See current listing
Model flexibility:
See full review for current model support
Key difference:
See the full Enclave AI review for a detailed comparison

Locally AI

Platforms:
iPhone/iPad/Mac
Price:
Free
Model flexibility:
Built on Apple MLX; access to Apple's on-device foundation model
Key difference:
Free alternative built specifically on Apple's MLX framework

Arbiter

Platforms:
See full review for current platform support
Price:
See current listing
Model flexibility:
See full review for current model support
Key difference:
See the full Arbiter review for a detailed comparison

LLM Farm

Platforms:
iOS/Mac (open source, GitHub: guinmoon/LLMFarm)
Price:
Free, open source
Model flexibility:
Load custom GGUF models via llama.cpp/ggml
Key difference:
Was pulled from the App Store and TestFlight in August 2025 per its own GitHub README β€” verify current availability before relying on it

Layla

Platforms:
iOS and Android
Price:
$19.99 plus in-app purchases
Model flexibility:
Custom GGUF models; character/roleplay focus with 100+ voices
Key difference:
Cross-platform (unlike Private LLM) but priced higher, with a roleplay/character focus

Maid

Platforms:
Cross-platform Flutter app (Android primary; also runs on other platforms Flutter supports)
Price:
Free, open source (MIT license)
Model flexibility:
Any GGUF file via llama.cpp; also connects to Anthropic, DeepSeek, Ollama, Mistral, OpenAI remotely
Key difference:
Free, fully open source, and not limited to local-only inference

RikkaHub

Platforms:
Android
Price:
Free, open source
Model flexibility:
Multiple cloud provider APIs plus local execution
Key difference:
Android-only; positions itself as a multi-provider client, not a local-first app

AnythingLLM Mobile

Platforms:
Android (iOS planned)
Price:
Free, open source
Model flexibility:
Runs GGUF models on-device via Cactus Compute (llama.cpp for React Native), or pairs with a self-hosted AnythingLLM server
Key difference:
Designed to pair with a self-hosted AnythingLLM workspace, not a standalone chat app

Platform, price, and feature details for third-party apps change frequently β€” verify current specifics on each app's own listing before deciding. LLM Farm's App Store availability in particular should be re-checked, since its own GitHub README described it as pulled from the App Store as of August 2025.

Who Should Use Private LLM

  • Apple-only users who want a polished, curated app and are fine paying once. iPhone, iPad, and Mac owners who value a maintained, actively updated app over assembling their own GGUF file collection get a large model library out of the box for $4.99.
  • Readers who value quantization quality over raw model count. If OmniQuant and GPTQ's claimed quality-per-bit advantage over round-to-nearest quantization matters to your use case, Private LLM is one of the few consumer apps built specifically around that approach.
  • Families sharing one Apple ID group. Family Sharing means a single $4.99 purchase can cover up to six people, which is cheaper per person than setting up several free apps individually for non-technical family members.
  • Users who want Siri/Shortcuts automation. The two App Intents let you wire local AI responses into existing iOS automations without opening the app.
  • Privacy-conscious users comfortable with a closed-source app. If "no data collected" per the App Store privacy label and "no account required" meet your bar without needing to audit the source code yourself, Private LLM's on-device architecture delivers that.

Who Should Not Use Private LLM

  • Android, Windows, or Linux users. Private LLM has no official app on any of these platforms β€” choose PocketPal AI (partial Android support), Maid, ChatterUI, or RikkaHub instead.
  • Readers who want a completely free option. $4.99 is inexpensive, but it is not free β€” PocketPal AI and Locally AI both cost nothing.
  • Readers who want to run the largest open-weight models. Mobile hardware limits what fits in memory β€” even the developer's own guidance caps most iPhones at 3B–8B parameter models; only a Mac with 48 GB+ unified memory reaches the 70B tier, and even that ceiling is far below the largest open-weight models available for server-class hardware.
  • Teams or organizations wanting a shared, centrally managed deployment. Private LLM is a single-user, single-device consumer app with no admin console, shared license management, or team billing β€” organizations should look at self-hosted, server-side local-LLM infrastructure instead.
  • Readers who want to audit the app's source code themselves. Private LLM is closed-source. If independent code review matters to you, an open-source alternative like PocketPal AI or Maid lets you verify behavior directly.

Frequently Asked Questions

How much does Private LLM cost?

Private LLM costs $4.99 as a one-time purchase on the Apple App Store, verified for this review on 2026-09-05. There is no subscription and no in-app purchases listed on the current App Store listing. App Store prices can vary by region and change over time β€” confirm the current price before buying.

Is Private LLM available on Android or Windows?

No official version exists on Google Play, the Microsoft Store, or any Linux package repository. Private LLM is built specifically for iPhone, iPad, and Mac. An unofficial beta APK has circulated outside the Play Store at points in the app's history, but it is not part of the developer's primary supported release channel, so this review treats Android as unsupported.

Who develops Private LLM?

Private LLM is developed by Numen Technologies Limited, which describes itself on its own site as a small, bootstrapped, two-engineer team without venture-capital funding.

What is OmniQuant, and why does Private LLM use it?

OmniQuant is an optimization-based quantization method that tunes the quantization range against calibration data, rather than using the simpler round-to-nearest (RTN) approach. Private LLM pairs OmniQuant with GPTQ for some models. The developer states this preserves more of a model's original output quality at a given bit-width than RTN quantization; this is the developer's own technical claim, not an independent PromptQuorum benchmark of Private LLM's specific model files.

Does Private LLM work completely offline?

Yes, once a model is downloaded. The app needs an internet connection only to download a model or an app update; chat, and macOS-specific services like grammar correction and summarization, run without a network connection afterward.

Does Private LLM collect any personal data?

Apple's App Store privacy nutrition label for this listing states the developer does not collect any data from the app. No account or sign-in is required to use it. This review relies on the App Store's privacy label rather than an independent audit of the app's closed-source code.

Does Private LLM sync chat history across devices via iCloud?

This is not documented in the developer's public FAQ. This review treats iCloud sync of conversations as unconfirmed rather than assuming it exists β€” verify current behavior directly in the app before relying on cross-device chat continuity.

What models can I run on an iPhone with Private LLM?

Per the developer's own guidance, most iPhones run 3B–4B parameter models comfortably (for example, Llama 3.2 3B or Qwen3 4B), while iPhone 15 Pro and newer can handle 8B-parameter models like Llama 3.1 8B. These are the developer's recommendations, not independently benchmarked results β€” actual performance depends on quantization level and available RAM.

How does Private LLM compare to PocketPal AI?

Private LLM is a paid ($4.99), closed-source, Apple-only app with a curated 140+ model library and OmniQuant/GPTQ quantization. PocketPal AI is free and open source, runs on iPhone/iPad with some Android support, and lets you import any GGUF file rather than choosing from a curated list. Choose Private LLM for a maintained, one-time-purchase experience with a wide built-in model library; choose PocketPal AI for a free, auditable, more manually configured setup.

Does one Private LLM purchase cover multiple devices or family members?

Yes. The $4.99 purchase covers iPhone, iPad, and Mac for the purchasing Apple ID, and Apple's Family Sharing extends that single purchase to up to six people in the same Family Sharing group.

Verdict

Private LLM earns its place among the better-documented mobile local-AI apps covered on this site: a clear $4.99 one-time price verified directly against the App Store, a curated library of more than 140 open-source models, and a specific, named quantization approach (OmniQuant plus GPTQ) rather than a vague "optimized for mobile" claim. The developer's own release notes show three years of continuous updates since the June 2023 launch, which is a meaningfully longer track record than several smaller apps in this cluster. The trade-offs are equally clear: it is Apple-only, it is not free, its model list is curated rather than fully open to custom GGUF imports, and its closed-source code means the "no data collected" claim rests on the App Store privacy label rather than independent code review. Readers who want a maintained, curated, one-time-purchase app across their Apple devices should buy it; readers on Android/Windows/Linux, readers who want a free option, or readers who want to audit source code themselves should start with PocketPal AI instead.

Sources

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