we really all are going to make it, aren't we? 2x3090 setup.

Reddit r/LocalLLaMA News

Summary

A user shares their experience setting up a dual 3090 GPU system to run the Qwen 3.6 27b model locally, achieving over 100 tokens/second after switching to Ubuntu and using the club-3090 tool with custom patches. They express excitement about the future of local AI.

i'm blown away. i saw someone made a post the other day about "club-3090" and after having sonnet patch some fixes into it, specifically a sse-session drop bug and a bug with tool-calling, it's fair to say that even "budget" setups like myself will have a path forward soon for only-local-ai. reference github: https://github.com/noonghunna/club-3090 (not mine) after getting this running, i was originally using WSL2. fair to say, it was "better" than LM studio but not quite good. t/s was like 30 and pp was around 400....i said fuck it and installed ubuntu as dual boot on the same machien (i'm just not very linux friendly when it's headless, prefer windows RDP) and wow. i'm getting like 4000 pp/s and 113 tk/s with no nvlink. supposedly, nvlink would make it faster..... either way, i'm very excited about this new local future. qwen 3.6 27b with 262k on 48 GB VRAM feels almost-sonnet level, and it's MUCH faster than cloud. and useful! I had it make some monkey patches and they work fantastic, and well as some relatively useful code reviews. im working now on making it work to handle my ssh sessions on my linux computers now. wondering what the next upgrade path could be. i was thinking about m5 ultra 512 GB + 4x DGX Sparks (prompt processing speeeeed) but now I'm wondering if we'll reach frontier class intelligence (maybe only domain specific) in smaller models in the next 12 months? awesome!
Original Article

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Wow! Qwen 3.6:35b-a3b on a 3090... pretty amazing.

Reddit r/artificial

A user shares impressive results running a quantized Qwen 3.6:35b-a3b model on a used RTX 3090, achieving 160 tokens per second output after fitting the model into VRAM, and demonstrates vision capabilities with a 75-second video processing time.