[sosodev]

I was testing the 4-bit Qwen3 Coder Next on my 395+ board last night. IIRC it was maintaining around 30 tokens a second even with a large context window.

I haven't tried Minimax M2.5 yet. How do its capabilities compare to Qwen3 Coder Next in your testing?

I'm working on getting a good agentic coding workflow going with OpenCode and I had some issues with the Qwen model getting stuck in a tool calling loop.

[lambda]

I've literally just gotten Minimax M2.5 set up, the only test I've done is the "car wash" test that has been popular recently: https://mastodon.world/@knowmadd/116072773118828295

Minimax passed this test, which even some SOTA models don't pass. But I haven't tried any agentic coding yet.

I wasn't able to allocate the full context length for Minimax with my current setup, I'm going to try quantizing the KV cache to see if I can fit the full context length into the RAM I've allocated to the GPU. Even at a 3 bit quant MiniMax is pretty heavy. Need to find a big enough context window, otherwise it'll be less useful for agentic coding. With Qwen3 Coder Next, I can use the full context window.

Yeah, I've also seen the occasional tool call looping in Qwen3 Coder Next, that seems to be an easy failure mode for that model to hit.

[lambda]

OK, with MiniMax M2.5 UD-Q3_K_XL (101 GiB), I can't really seem to fit the full context in even at smaller quants. Going up much above 64k tokens, I start to get OOM errors when running Firefox and Zed alongside the model, or just failure to allocate the buffers, even going down to 4 bit KV cache quants (oddly, 8 bit worked better than 4 or 5 bit, but I still ran into OOM errors).

I might be able to squeeze a bit more out if I were running fully headless with my development on another machine, but I'm running everything on a single laptop.

So looks like for my setup, 64k context with an 8 bit quant is about as good as I can do, and I need to drop down to a smaller model like Qwen3 Coder Next or GPT-OSS 120B if I want to be able to use longer contexts.