Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone
In plain words
This tool enables users to run large language models locally on resource-constrained devices. It allows an 80-billion-parameter Qwen model to operate on a Mac with only 4.3 GB of RAM, and a 35-billion-parameter version on an iPhone. The project is designed for developers and enthusiasts who want to use advanced AI models without relying on cloud services or high-end hardware, making large language models accessible on personal devices.
written from the facts on this page · September 2026
Does the same job
all alternatives →- Q6Qwen3.6-35B-A3B on a 16 GB M1 Pro with SSD-streamed MoEJul 2026 · github.com · ▲24
Running 104GB Qwen3.8-Flash-Next on 48GB Mac with at ~12 tok/s5d ago · github.com · ▲238Run Qwen3.8-Flash-Next (125B MoE, 104 GB at 4-bit) on Macs with a fraction of that RAM by streaming experts from SSD. MLX + Swift, Ollama-compatible API. - carloslfu/slotstream
- OSOpen-source engine running Gemma 4 26B in 2 GB RAM on any M-series MacJul 2026 · github.com · ▲919
Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
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