Comment by 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.

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.

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)

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