Offline, free, lightweight mobile LLM. Is it actually real?
I’m genuinely curious. Has anyone shipped an offline, free, lightweight mobile LLM, especially for a speech-based app?
I’ve tried building an on-device AI assistant, and the reality is messy:
- Models are still huge
- Mobile tooling is painful (Android + JNI + assets)
- Latency and memory constraints are real
- “Lightweight” feels like a myth unless you compromise hard
So I’m asking the community:
Is there a truly usable offline (and free of cost) LLM for mobile right now?
If yes, what did you use and how did you ship it?
If no, what’s the closest thing you’ve tried?
Top comments (7)
“Yep — offline mobile LLMs exist, but you usually have to compromise on size, speed, or accuracy. Quantized LLaMA/GGUF models are the closest I’ve seen work on-device.”
I checked it too. We hope it improves. It would be helpful.
yes, i have used gemma 3 model and it works flawlessly.
Yes — there are usable offline, free, lightweight mobile LLMs in the wild (e.g., running quantized LLaMA, Mistral 7B, or GGML-based variants on device), but getting them performant for speech without significant compromises in latency/accuracy is still nontrivial and depends heavily on aggressive quantization and model choice.
Most shipped examples lean on frameworks like GGML/llama.cpp with 4-bit quantization or similar, and integrate small local encoders/decoders for speech; if you need higher accuracy or larger context, you still need to accept tradeoffs or offload to the cloud.
Totally agree. This is pretty much why I asked the question.
I actually tried shipping this for a speech first mobile app and wrote up the whole journey in an earlier post called Offline AI on Mobile: Tackling Whisper, LLMs, and Size Roadblocks.
What I found in practice:
So yeah, it is real, but only if you accept very tight constraints around context, speed, or response quality. Anything that feels like a smooth assistant still involves pretty visible trade offs.
Yes, there are several small LLMs perfect for running locally on your phone with solid performance.
Top Picks
These models (under 4B params) fit in 4-8GB RAM and hit 5-15 tokens/sec on modern devices like recent Pixels or iPhones.
How to Run
Grab MLC LLM or PocketPal from app stores, download quantized GGUF versions from Hugging Face, and load 'em up—no cloud needed. Start small to test speed!
Yep, those are exactly the models I tested.
They are impressive on their own, but moving from a chat demo to an offline speech based app is where the cracks show. I documented the full attempt here if you are interested: Offline AI on Mobile: Tackling Whisper, LLMs, and Size Roadblocks.
A few things that caught me out:
Tools like MLC LLM and PocketPal definitely help, but shipping this inside a real app still meant choosing between speed, size, or quality. Never all three.
Feels like we are close, just not quite there yet for offline, speech first experiences.