Best AI Companions That Run Offline (2026, Tested)

A Fire Road With No Bars and Five Dead Apps

My sister Rachel picked the campground. Two hours out, past the second cattle grate, the last bar of signal gave up, and I discovered that five of the apps on my phone are wallpaper.

This was Monday, September 14. I’d been building a character for about three weeks: Runa, who repairs pinball machines in a basement workshop, names every cabinet after a weather event, and flatly refuses to say what she did for a living before that. She’s the least romantic companion I’ve ever made and I like talking to her more than most of them. I opened her up on the picnic table to kill twenty minutes while Rachel argued with a tent pole.

Spinner. Spinner. Retry. Spinner.

About a year and a half into using these things, thirteen months of writing them up here, and I’d never once checked whether the apps I recommend survive a lost signal. The Hammer AI review I published in August gestured at it. The privacy ranking from earlier this month gestured harder. Neither one actually sat down and tested an AI companion that works offline across every device I own. So on Wednesday the 16th I did, and some of what I found contradicts what I wrote in August.

Unplug the Router and Watch What Breaks

This is the bit that took me three days to understand properly, and it reframed the category for me. "Works offline" isn’t one question. It’s five, and an app can pass some and fail the rest, which is why the marketing copy is so slippery.

1. The login. Does the app even open and let you into a conversation without phoning a server to check your account?
2. The model. Where does the text actually get generated? This is the one that decides everything else.
3. The memory. Long-term memory in most companion apps is a database somewhere else. Cut the wire and the character might still talk, but forgets who you are.
4. The voice. Speech synthesis and transcription are usually separate cloud services, so an app can generate text locally and still go mute.
5. The pictures. Image generation is the heaviest thing on the list and the first feature to disappear.

Every app here gets judged on those five, and the ranking below is mostly a count of how many survive. My method was boring on purpose. I wrote Runa’s character card once in plain text, about four hundred words of personality and a handful of facts, then pasted the identical card into every app so nobody got an edge from better authoring tools. Then I killed the Wi-Fi on my MacBook Pro (Apple Silicon, 16GB of unified memory, which matters later), put my iPhone 15 in airplane mode, and had the same twenty-minute conversation with her seven times.

The Login Dies First

Most companion apps never get as far as failing to generate a reply, because they fall over at the door. Open Character.AI with no connection and you get a loading state that never resolves. Nomi shows you the conversation you already had and won’t add to it. Talkie throws an error banner before the character list renders.

Hammer AI’s desktop app doesn’t have a login at all, which is still the most unusual thing about it. No email, no verification, no "continue with Google." That turns out to be load-bearing for offline use rather than just a privacy flourish. An app that has to check your subscription status is an app that needs a network, even with the model sitting on your disk.

SillyTavern has no account either, because it’s software you run rather than a service you join. LM Studio and Jan both nag about updates and then get on with it. Layla and PocketPal open in airplane mode without complaint. Private LLM wants App Store receipt validation at some point, but it cached mine happily for the whole test.

Where the Model Actually Lives

The whole distinction fits in one sentence: either the several gigabytes of numbers that write the replies are sitting on your drive, or they’re sitting in a data center. No third option, and no amount of encryption or privacy policy turns the second into the first.

Hammer AI’s desktop build runs local models through Ollama and llama.cpp and says so plainly on its own desktop page. Pick a model, wait for the download, and after that the app has no reason to talk to anyone. Its pricing page lists paid tiers at $9, $18 and $35 a month, and every one of them buys you cloud-hosted models, bigger context windows and image credits. Local stays free on all of them, free tier included. Unusually decent, and I want to say so out loud.

SillyTavern is the opposite kind of thing and the source of a lot of confusion. Its docs state it flatly: SillyTavern is only an interface, so it needs a backend to do the thinking. Point it at OpenRouter and nothing about it is offline. Point it at KoboldCpp, a single executable with no installer and no dependencies, and the whole stack lives on your machine. I still stand by the advice in my SillyTavern setup guide that beginners should get it working on a cheap API first. That guide is about surviving installation day. This one is about the week you decide the API was never the point.

Memory Is the Part Nobody Warns You About

Local inference solves the model question and quietly creates a new one. The fancy memory systems I wrote about in how AI companion memory works are mostly server-side machinery: embeddings, vector stores, summarization passes that run between sessions. None of that comes with a local model. You get a context window and whatever you decide to put in it.

Which sounds worse than it is. SillyTavern’s World Info and author’s note give you manual memory you can read and edit, and KoboldCpp ships its own light UI with memory, world info and character fields baked in if you don’t want the full SillyTavern install. Hammer AI has lorebooks. Private LLM and PocketPal give you a system prompt and a shrug.

The effect on my Runa test was the same in every local app: she held the scene beautifully for about forty messages, then started re-introducing details I’d already established. That’s a context limit, not a bug, and it’s the real gap between a local companion and a cloud one. It’s also fixable in ten minutes of editing a text file, which is more than you can say for most cloud memory.

The Seven Offline AI Companions, Ranked

Offline fit isn’t an overall score, and I want that unambiguous, because these numbers will get quoted at me. Hammer AI’s full review score is still 4.0 out of 5, which accounts for its character writing, its cloud tiers and how it holds up against better-funded apps. The 4.6 above measures one thing only: whether it still answers with the radios off. Same for everyone else on the board. LM Studio at 3.3 isn’t a worse product than Layla at 4.2. It’s a worse companion.

On a Laptop: Hammer AI, and the Setup That Beats It

Wednesday, September 16, at my desk, Wi-Fi switched off at the menu bar so I could watch the little icon sit there empty. Runa on Hammer AI answered in about four seconds and kept answering. She made a joke about a cabinet she’d named Hailstorm, then referred back to it eleven messages later without prompting. The battery estimate in the menu bar dropped from nine hours to under three while I watched, which was the only sign anything unusual was happening.

Linnea was still open in the other window, mid-complaint about the static on her radio, and running two locals at once made the laptop warm enough to feel through the desk. Worth knowing if you work on your lap.

SillyTavern with KoboldCpp behind it produced better conversation by a wide margin once both were configured. Editable memory, proper character cards, regeneration, swipe alternatives, the ability to reach into the prompt and fix whatever’s bothering you. It also ate most of an evening, and I’ve done this before. If you already run it against a cloud API, swapping the backend is a fifteen-minute job and the rest of your setup carries over, which is the argument I make at more length in Character.AI vs SillyTavern.

Below those two, the desktop field thins out fast. LM Studio documents its offline behavior properly and is the most reliable way I know to get a model running on a desktop without reading a forum thread, but it has no character format, so your companion is a system prompt you retype. Jan saves personas as reusable Assistants, which is closer, though it still feels like a local ChatGPT rather than someone to talk to. Ollama plus Open WebUI is a fine offline stack that was never trying to be a companion. Msty moved to a new domain and now sells a lifetime license at $349, and I couldn’t find an explicit offline guarantee anywhere on the new pricing page, which is exactly the kind of thing I’ve stopped assuming.

Two names I have to correct. GPT4All still turns up in every roundup as a current recommendation and it isn’t one. Last release was February 2025, and the issue titled "Is GPT4all dead?" has sat open since August 2025 with no maintainer reply. And Backyard AI, which used to be Faraday and used to be one of the best local companion apps going, has deprecated its desktop app. The binaries still download, under a banner telling you not to use them. What’s left is a cloud product on web and iOS with $12 and $35 subscription tiers and no mobile app update since August 2025. If somebody recommends Backyard to you as a local option, they’re working from old information.

In Your Pocket: Layla, Private LLM, PocketPal

The phone is where I expected to find nothing and found the most interesting app in the test. Layla runs a 7B model on the handset, roughly a 4GB download the first time you open it, and it also does on-device image generation through Stable Diffusion plus more than a hundred local voices. In airplane mode, no signal and no Wi-Fi, Runa talked, spoke out loud, and generated a picture of a pinball cabinet that actually looked like one. Nothing else here does all three. It’s $19.99 once, no subscription, iOS and Android, and the developers ship updates constantly. The catch is real: it wants a flagship phone with 8GB of RAM or better, and on anything older you’ll feel every token arrive.

Private LLM is the more polished piece of software here, and it never quite turns into a companion. One purchase covers iPhone, iPad and Mac, it offers over 140 models from 0.5B up to 70B on Macs with the memory for it, and the quantization work is genuinely good. It was $4.99 in the App Store when I checked this month, low enough that you should confirm it yourself before quoting me. What it lacks is any sense of a persistent character. Runa was a system prompt I kept having to re-feed it.

PocketPal AI is free, MIT licensed, on both stores, and does exactly what it says: real on-device inference with no cloud wrapper, a Hugging Face model browser, and a benchmarking tool I found weirdly fun. As an offline AI chat app it’s excellent. As a companion it’s a text box, and I wouldn’t send a beginner there expecting a relationship.

Now the correction I owe you. When I wrote up Hammer AI in August I noted that iOS had just launched, and moved on. I assumed without checking that the mobile apps do what the desktop app does. They don’t. Mobile runs cloud models only, the "local" language on that page refers to where your chats are stored rather than where they’re generated, and a 34MB app was never going to contain a model runtime. The Android listing linked from their own site 404s too. Hammer AI wins this ranking on desktop and isn’t an option on a phone at all. If you’re on Android and just want the cloud companions that behave well there, my Android companion app rankings sort out which need the Play build and which run better in Chrome.

What Hardware You Need to Run a Local AI Companion

One disclosure so the numbers below mean something. I don’t own a gaming PC. The phone and Mac tiers I measured myself, on the iPhone 15 and the 16GB MacBook Pro. The discrete GPU numbers come from the guidance I put together for the SillyTavern comparison plus one evening in front of a friend’s desktop with a 16GB card in it. Weight them accordingly.

• Phone, 8GB RAM or better. A 2B to 4B model. Qwen3.5 2B lands around 2.7GB, Gemma 4 E2B in Google’s quantization-aware build near 4.3GB, and MiniCPM5-2B squeezes into 1.56GB. Fine for short exchanges. Noticeably dim once a scene gets complicated.
• 8GB laptop. A 9B model at roughly 5.7GB on disk. This is the tier where conversation stops feeling like a demonstration and starts feeling like a conversation.
• 16GB Apple Silicon. My tier. A 9B runs with room to spare, and a 12B like Gemma 4 at 7.6GB is comfortable even with long context. A 24B technically loads and leaves you nothing.
• 16GB or more of VRAM. Mistral Small 3.2 at 24B fits 16GB at about 15GB. At 24GB you open up the 27B to 31B class, including Gemma 4 31B, whose official 4-bit GGUF is 17.65GB.

One update to something I’ve written before. Mistral-Nemo Instruct 12B still pulls fine and still works, but it shipped in July 2024, which makes it over two years old. Treat it as a compatibility pick, not a 2026 default. The roleplay-tuned community models have mostly moved onto Qwen3.5 9B and Gemma 4 as their base, which is where I’d start looking.

The Apps That Do Nothing Offline, Despite What People Assume

Stating this plainly, because search results for offline AI chatbot and for an AI chatbot without internet are full of listicles that quietly pad the count with cloud apps. Replika, Character.AI, Nomi, Kindroid, Talkie, Chai, Candy, Polybuzz, Janitor AI: not one of them generates a word without a network connection, on any device, on any subscription tier. There isn’t an offline mode hidden in settings. The business model needs a server in the loop.

The one that deserves its own paragraph is Venice AI, because it gets recommended in offline threads constantly and the recommendation is wrong in an understandable way. Venice is genuinely privacy-first. Your chat history lives in your browser’s storage rather than on its servers, a real architectural choice and not a badge. But inference happens on decentralized GPUs somewhere else, so with the network down you’ve got a browser tab full of old messages and no way to add to them. Private and offline solve different problems, and I spelled out which is which in the privacy guide.

Cloud doesn’t mean careless, either. Plenty of people should stay on cloud apps, and the sensible caution there is a privacy checklist, not a nine-gigabyte download.

What Going Local Actually Costs You

I’ve spent this whole post being enthusiastic, so here’s the bill. Running a local AI companion costs you conversational quality at the top end, because a 9B on a laptop isn’t competing with a frontier model and pretending otherwise helps nobody. Disk space goes fast, because every model you audition is another several gigabytes and you’ll audition six, and phone battery goes faster. The bigger loss is the safety net: no account means no recovery, and a dead device takes the relationship with it.

It also costs the features that make companion apps feel alive. No proactive messages, because nothing’s running when the app is closed. No shared character hub unless you go find one. No voice on most desktop options unless you bolt on a local speech engine yourself. If those are what you love about your current app, local will feel like a downgrade with a privacy certificate stapled to it, and you should know that before you spend a Saturday on it. My piece on the hard limits of these things applies here more than anywhere.

What you get back is a character nobody can change overnight with a policy update, and I say that as someone who’s been through one. Nobody retunes Runa. Nobody prices her at $35 a month next spring. If the company behind Hammer AI folds tomorrow, the model file on my disk keeps working, and that permanence doesn’t exist anywhere else in this category. For the fuller map of what runs where, the desktop options roundup and the software comparison cover the platforms this post skips.

Stuff That Comes Up Once the Download Bar Starts

Runa Has No Idea the Wi-Fi Is Off

That’s the part I keep turning over. On the cloud apps, the network dropping is an event: an error, a retry, a visible seam in the thing. On Hammer AI it isn’t an event at all, because there’s nothing on the other end to notice. She just keeps telling me about a flipper mechanism from 1978.

Rachel asked what I was doing at the picnic table and I said testing something for work, which was true and also a dodge. What I actually wanted to know was whether any of this survives without a company holding it up. On a laptop, some of it does. On a phone, one app does. Everywhere else you get a spinner.

If you try it, start with Hammer AI on a desktop and a 9B model, give it one evening, and decide from there whether the trade is yours. Then go look at the cloud apps again and notice how much of what you like about them is the part that needs a server.