Prefill is survivable if you cache well. But what kills me is the context. Qwen 27 needs a ton of room for KV Cache. I guess not an issue on a 128 GB Halo or Spark, but if you are running of consumer/prosumer GPUs it's miserable to be compacting every 120k tokens.
replyWhat type of agentic tasks are you using it for (eg how complex)?
replyFor personal stuff, I use it with AnythingLLM. It replaced any Google search for me. For coding, I run opencode though I have been debating switching to Pi. I would argue it’s at Sonnet 3 level.
replyI find that 35B-A3B is much easier to run on my M4 Max (both prefill and generation)
replyIt's well known 35b is much faster (on any hardware) and quite a bit dumber
replyThis really very much depends on how you are using it, I think. If you intend to leave it to solve long context problems and write whole prototypes, the 27B is going to be much better.
replyBut if you are sort of pair-programming with the model, the speed obviously matters and I think then the 35B is acceptably smart, and when it's wrong it'll be wrong much more quickly. It seems very good on SQL and PHP, and I assume on typical JS and Python.
I would rather work that way, so I hope they do produce a small MoE model.
How are you running it on a Strix Halo? The weights aren't out yet, are they?
replyI interpret @syntaxing as meaning they are looking forward to running Qwen3.8-27B, but are frustrated by prefill times with other models, such as Qwen3.6-27B.
replyI meant Qwen3.6. Unsloth supposedly has early preview of the model and the VRAM requirement is the same so most people expect similar model size and type.
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