[lambda]

You don't even need to go this expensive. An AMD Ryzen Strix Halo (AI Max+ 395) machine with 128 GiB of unified RAM will set you back about $2500 these days. I can get about 20 tokens/s on Qwen3 Coder Next at an 8 bit quant, or 17 tokens per second on Minimax M2.5 at a 3 bit quant.

Now, these models are a bit weaker, but they're in the realm of Claude Sonnet to Claude Opus 4. 6-12 months behind SOTA on something that's well within a personal hobby budget.

[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.

[lambda]

After some more testing, yikes, MiniMax M2.5 can get painfully slow on this setup.

Haven't tried different things like switching between Vulkan and ROCm yet.

But anyhow, that 17 tokens per second was on almost empty context. By the time I got to 30k tokens context or so, it was down in the 5-10 tokens per second, and even occasionally all the way down to 2 tokens per second.

Oh, and it looks like I'm filling up the KV cache sometimes, which is causing it to have to drop the cache and start over fresh. Yikes, that is why it's getting so slow.

Qwen3 Coder Next is much faster. MiniMax's thinking/planning seems stronger, but Qwen3 Coder Next is pretty good at just cranking through a bunch of tool calls and poking around through code and docs and just doing stuff. Also MiniMax seems to have gotten confused by a few things browsing around the project that I'm in that Qwen3 Coder Next picked up on, so it's not like it's universally stronger.

[sosodev]

Thanks for the additional info. I suspected that MiniMax M2.5 might be a bit too much for this board. 230B-A10B is just a lot to ask of the 395+ even with aggressive quantization. Particularly when you consider that the model is going to spend a lot of tokens thinking and that will eat into the comparatively smaller context window.

I switched from the Unsloth 4-bit quant of Qwen3 Coder Next to the official 4-bit quant from Qwen. Using their recommended settings I had it running with OpenCode last night and it seemed to be doing quite well. No infinite loops. Given its speed, large context window, and willingness to experiment like you mentioned I think it might actually be the best option for agentic coding on the 395+ for now.

I am curious about https://huggingface.co/stepfun-ai/Step-3.5-Flash given that it does parallel token generation. It might be fast enough despite being similar in size to M2.5. However, it seems there are still some issues that llama.cpp and stepfun need to work out before it's ready for everyday use.

[nyrikki]

It is crazy to me that it is that slow, 4 bit quants don't lose much with Qwen3 coder next and unsloth/Qwen3-Coder-Next-UD-Q4_K_XL gets 32 tps with a 3090 (24gb) as a VM with 256k context size with llama.cpp

Same with unsloth/gpt-oss-120b-GGUF:F16 gets 25 tps and gpt-oss20b gets 195 tps!!!

The advantage is that you can use the APU for booting, and pass through the GPU to a VM, and have nice safer VMs for agents at the same time while using DDR4 IMHO.

[lambda]

Yeah, this is an AMD laptop integrated GPU, not a discrete NVIDIA GPU on a desktop. Also, I haven't really done much to try tweaking performance, this is just the first setup I've gotten that works.

[nyrikki]

The memory bandwidth of the Laptop CPU is better for fine tuning, but MoE really works well for inference.

I won’t use a public model for my secret sauce, no reason to help the foundation models on my secret sauce.

Even an old 1080ti works well for FIM for IDEs.

IMHO the above setup works well for boilerplate and even the sota models fail for the domain specific portions.

While I lucked out and foresaw the huge price increases, you can still find some good deals. Old gaming computers work pretty well, especially if you have Claude code locally churn on the boring parts while you work on the hard parts.

[lambda]

Yeah, I have a lot of problems with the idea of handing our ability to write code over to a few big Silicon Valley companies, and also have privacy concerns, environmental concerns, etc, so I've refused to touch any agentic coding until I could run open weights models locally.

I'm still not sold on the idea, but this allows me to experiment with it fully locally, without paying rent to some companies I find quite questionable, and I can know exactly how much power I'm drawing and the money is already spent, I'm not spendding hundreds a month on a subscription.

And yes, the Strix Halo isn't the only way to run models locally for a relatively affordable price; it's just the one I happened to pick, mostly because I already needed a new laptop, and that 128 GiB of unified RAM is pretty nice even when I'm not using most of it for a model.

[cowmix]

If you don't mind saying, what distro and/or Docker container are you using to bet Qwen3 Coder Next going?

[lambda]

I'm running Fedora Silverblue as my host OS, this is the kernel:

  $ uname -a
  Linux fedora 6.18.9-200.fc43.x86_64 #1 SMP PREEMPT_DYNAMIC Fri Feb  6 21:43:09 UTC 2026 x86_64 GNU/Linux

You also need to set a few kernel command line paramters to set it up to allow it to use most of your memory as graphics memory, I have the following in my kernel command line, those are each 110 GiB expressed in number of pages (I figure leaving 18 GiB or so for CPU memory is probably a good idea):

  ttm.pages_limit=28835840 ttm.page_pool_size=28835840

Then I'm running llama.cpp in the official llama.cpp Docker containers. The Vulkan one works out of the box. I had to build the container myself for ROCm, the llama.cpp container has ROCm 7.0 but I need 7.2 to be compatible with my kernel. I haven't actually compared the speed directly between Vulkan and ROCm yet, I'm pretty much at the point where I've just gotten everything working.

In a checkout of the llama.cpp repo:

  podman build -t llama.cpp-rocm7.2 -f .devops/rocm.Dockerfile --build-arg ROCM_VERSION=7.2 --build-arg ROCM_DOCKER_ARCH='gfx1151' .

Then I run the container with something like:

  podman run -p 8080:8080 --device /dev/kfd --device /dev/dri --security-opt seccomp=unconfined --security-opt label=disable --rm -it -v ~/.cache/llama.cpp/:/root/.cache/llama.cpp/ -v ./unsloth:/app/unsloth llama.cpp-rocm7.2  --model unsloth/MiniMax-M2.5-GGUF/UD-Q3_K_XL/MiniMax-M2.5-UD-Q3_K_XL-00001-of-00004.gguf --jinja --ctx-size 16384 --seed 3407 --temp 1.0 --top-p 0.95 --min-p 0.01 --top-k 40 --port 8080 --host 0.0.0.0 -dio

Still getting my setup dialed in, but this is working for now.

Edit: Oh, yeah, you had asked about Qwen3 Coder Next. That command was:

  podman run -p 8080:8080 --device /dev/kfd --device /dev/dri --security-opt seccomp=unconfined --security-opt label=disable \
    --rm -it -v ~/.cache/llama.cpp/:/root/.cache/llama.cpp/ -v ./unsloth:/app/unsloth llama.cpp-rocm7.2  -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q6_K_XL \
    --jinja --ctx-size 262144 --seed 3407 --temp 1.0 --top-p 0.95 --min-p 0.01 --top-k 40 --port 8080 --host 0.0.0.0 -dio

(as mentioned, still just getting this set up so I've been moving around between using `-hf` to pull directly from HuggingFace vs. using `uvx hf download` in advance, sorry that these commands are a bit messy, the problem with using `-hf` in llama.cpp is that you'll sometimes get surprise updates where it has to download many gigabytes before starting up)

[nyrikki]

I can't answer for the OP but it works fine under llama.cpp's container.