Dario Amodei asked for a slowdown on artificial intelligence and a ban on open source AI development. The reaction was, luckily, overwhelmingly on the opposite side: Jensen Huang of Nvidia, and even heads of state, like Donald Trump and Xi Jinping, came out in favor of keeping AI development — and open source — moving. So, if open weights are staying, then a new question arises: which models can you run comfortably on a medium laptop in September 2026?

I travel a lot and I want intelligence on the go, on hardware I actually own.

This video below is my short answer to: what “open weights” really means, how much RAM you need, how quantization works, and my very own shortlist with models from Europe, the US, and China.

Watch the video if you want detailed info, if not, here’s the TLDR:

My shortlist as of September 2026

• Qwen 3.8 27B — my “download tonight” all-rounder
• Google Gemma 3 (4B / 12B / 27B) — start small, scale up
• Mistral Magistral Small (24B) — Europe’s practical open option (and a bit of a surprise)
• NVIDIA Nemotron Light / Lightning — if you already have an NVIDIA GPU
• Meta Muse Glimmer (~30B distilled) — fallback when you have ~32GB+ RAM

On a stronger Mac (often 64GB+), DwarfStar from antirez can push you into DeepSeek V4 / GLM 5.3 / Qwen territory that would otherwise want DGX Spark-class hardware.

General hardware requirements: you’d want a silicon Mac (Mac Book Pro or Mac Mini Studio) or an Nvidia based Intel machine (DGX Spark, Framework, etc). Memory rule of thumb: 16GB barely fits and it’s a basic start, 32GB you can actually do useful things, more is always better. You’ll almost always run a 4-bit / 3-bit / 2-bit quantized, not the full file that the labs released.

This whole thing is not only about speed, or tokens per second. Local means your prompts, chat history, and cache stay on your machine. You can run inference even without the internet. This time around, the ban on open source failed, but who know, it may succeed in the future. Knowing how to set up your own local AI is your insurance against this.

There’s also a full series about how to choose an open weights model in general. You may want to start here: How To Choose An Open Weights Model – Episode 1

And for more benefits: Running Local, Private AI Models, When and Why.