I just hit that error a few minutes ago. I build my llama.cpp from source because I use CUDA on Linux. So I made the mistake of trying to run Gemma4 on an older version I had and I got the same error. It’s possible brew installs an older version which doens’t support Gemma4 yet.
The 14B Q4_K_M needs 9GB, but Q3_K_M is 7.3GB. But you also need some room for context. Still, maybe using `--override-tensor` in llama.cpp would get you a 50% improvement over "naively" offloading layers to the GPU. Or possibly GPT-OSS-20B. It's 12.1GB in MXFP4, but it’s a MOE model so only a part of it would need to be on the GPU. On my dedicated 12GB 3060 it runs at 85 t/s, with a smallish context. I've also read on Reddit some claims that Qwen3 4B 2507 might be better than 8B, because Qwen never released a "2507" update for 8B.
I wasn't sure where I'd seen that "retiring" spiel before, but then I remembered someone was (still is) selling a handmade jewelry website claiming $4.3M revenue and $1.3M profit.
I use an even older Macbook and an even older macOS. Of course, the browsers no longer work with the latest JS, so occasionally when I need to use some webapp I boot up a Linux VM and do what I need to do. With limited RAM even that's a pain, but it works for now.
Can confirm. I was trying to send the newsletter (with SES) and it didn't work. I was thinking my local boto3 was old, but I figured I should check HN just in case.
I have an RTX 3060 with 12GB VRAM. For simpler questions like "how do I change the modified date of a file in Linux", I use Qwen 14B Q4_K_M. It fits entirely in VRAM. If 14B doesn't answer correctly, I switch to Qwen 32B Q3_K_S, which will be slower because it needs to use the RAM. I haven't tried yet the 30B-A3B which I hear is faster and closer to 32B. BTW, I run these models with llama.cpp.
For image generation, Flux and Qwen Image work with ComfyUI. I also use Nunchaku, which improves speed considerably.