Beats how? In my experience 1-bit bonsai 27B is quite "dumb" when asked a question about a lot of things, as a canned repository of static knowledge from its training dataset. I mean, I literally asked it for a 250 word description of Seattle and it hallucinated a tallest building in the city with an observation deck that doesn't exist, and didn't mention the Space Needle.
Really basic stuff. But then again, the entire thing was running in <6GB of RAM.
But before anyone says 1-bit bonsai 27B beats anything, please actually run it and ask it some questions about topics you already know the answer to.
While Qwen 3.6 35B A3B in Q8 with full context capability (llama-server in no-mmap mode with 262k context will eat 47GB, so not comparable in size either) knows a great deal. The 35B-A3B can even translate multiple pages of English into Farsi and its Farsi output is not far off the quality of what Google Translate does.
Really basic stuff. But then again, the entire thing was running in <6GB of RAM.
But before anyone says 1-bit bonsai 27B beats anything, please actually run it and ask it some questions about topics you already know the answer to.
While Qwen 3.6 35B A3B in Q8 with full context capability (llama-server in no-mmap mode with 262k context will eat 47GB, so not comparable in size either) knows a great deal. The 35B-A3B can even translate multiple pages of English into Farsi and its Farsi output is not far off the quality of what Google Translate does.
I haven't tested something as badly quantized as 35B A3B Q2 which is somewhere around 12GB on disk. https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF