I have a similar machine, and briefly poked at running a local LLM, but got discouraged after a couple days. The quality, responsiveness, and impact on the rest of the system didn’t seem worth it to me.
replyWhat sorts of things are you doing with the local LLM? Anything interactive? Should I take another look?
Yes, 15-30 t/sec is pretty slow for local models so I recommend running local LLM tasks overnight where (vs paid plans) there isn't a risk of chewing through your token budget from a rogue loop or sub-agent. Even if it takes hours, you're sleeping anyway so no concern. herdr + pi works great for this but there are lots of harnesses.
replyMemory used : 38GB, and I haven't even started a LLM nor podman, I always fight with memory when using LLM on my mac with 48gb.
replyAnd I don't remember to have been able to have pushed to 200k context Qwen 3.6. 3.8 is running on my RTX 5090.
Qwen 27B runs very comfortably on a 5090. You need to use Q4 quants and Q8 KV cache. Here's the math
replyI assume you mean Qwen 3.8-27B? Yes, you can run this in 32GB of RAM, but it's very context limited. With KV cache compression and other techniques, it's better now than in the past, but I'd still want more RAM, personally.
replyEDIT to add that you need to reserve 8GB for the system if you don't want to cause problems on macOS, which means 32GB RAM = 24GB max for model + context. It takes 18-19GB to load a 4-bit quant of Qwen3.8-27B, so I'd be really surprised if you can actually get a 200k context window. You need to fit within a 24GB WSS (which is generally a more constrained RSS) to get stable performance on 32GB RAM.
I run Qwen 3.8 27B just fine on my Mac mini M4 24GB. I use Unsloth's Q3 XXS with 128k context. It successfully completes long horizon tasks with OpenCode.
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