The Bonsai quantizations are not good for real work. I have tried them. They are as bad as you would expect from 2-bit quants. They may have done a good job quantizing to that limit, but it’s still bad.
Even the 4-bit Qwen3.8 27B quants are still iffy for coding work. It’s not until Q5 or Q6 that I find them useful, which is too bad because that cuts into available context on 32GB cards.
> Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.
I experiment with a lot of local inference engines and I also use both Fable and Astra. I wish this was true, but it’s just not. I can generate tens of millions of Qwen3.8 tokens per day and run the hardware nonstop, but even brute forcing it like this just can’t accomplish the same thing that I can get Astra, Fable, or even Opus and Sol to get done in an hour or less most of the time. You just cannot make up for the lack of intelligence built into the model by brute force unless maybe your timeline extends to infinity.
The swarm strategy can’t overcome the dumbed down model problem. You just end up with your swarm of agents all going in the same dumbed down loops.
5GB of bonsai weights end up being similar to 5GB of QAT weights even when one is derived from a 27B and the other from an 8B