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[ARTICLE · art-107386] src=forum.level1techs.com ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

What i run with my Strix Halo

A user reports running large language models on a 128GB Bosgame M5 Strix Halo mini PC purchased used for 1800€ on eBay, achieving 30 tokens per second decode with Qwen 3.8 27B and 50 tokens per second with Ling 3.0 Flash using the ROCmFPX llama.cpp fork with ROCmFP4 quantization. The user exchanged thermal paste with Arctic MX7 to reduce idle fan noise and runs Proxmox 9 with ROCm 7.13 from the AMD repository.

read2 min views1 publishedAug 22, 2026

This is my very first post, hello there.

So my Strix Halo is a 128GB Bosgame M5. I got it used for 1800€ at Ebay. I didn’t do any hardware changes to it yet, except exchanging the thermal paste with Arctic MX7, which brought my idle temps down a bit. Before the fan was audible every few minutes, now it’s silent in idle. Worth it.

I run Proxmox 9 and ROCm 7.13 from the AMD repository. A LLM LXC container has Debian 13 and Docker from the Docker repository installed. It runs another LXC with Hermes and a VM with some other stuff, 8GB RAM.

For extra speed i installed the ROCmFPX llama.cpp fork and use quantization to the ROCmFP4 data type, a must have if you use Strix Halo.

Here is my start command for Qwen 3.8 27b, i get 30 t/s decode:

/root/ROCmFPX/build-strix-rocmfp4/bin/llama-server \
  --hf-repo kingjones777/Qwen3.8-27B-ROCmFP4-STRIX-MTP-GGUF --model Qwen3.8-27B-Q4_0_ROCMFP4_STRIX.gguf --alias Qwen-3.8-27b \
  --hf-repo-draft kingjones777/Qwen3.8-27B-ROCmFP4-STRIX-MTP-GGUF --model-draft mtp-Qwen3.8-27B-Q4_0.gguf --spec-type draft-mtp --spec-draft-ngl 99 --spec-draft-device ROCm0 --spec-draft-n-max 4 --spec-draft-n-min 0 --spec-draft-p-min 0.0 \
  -ngl 999 --jinja -fit off --parallel 2 -dev ROCm0 \
  --ctx-size 131072 --flash-attn on --kv-unified --cache-type-k q8_0 --cache-type-v q8_0 --cache-type-k-draft q8_0 --cache-type-v-draft q8_0 \
  --cache-ram 4096 \
  --reasoning-budget 16384 --reasoning-budget-message " ... thinking budget exceeded, let's answer now." \
  --no-mmap --host 0.0.0.0 --port 1338 \
  --metrics

Another model i run is Ling 3.0 Flash. I get 50 t/s decode with this config:

/root/ROCmFPX/build-strix-rocmfp4/bin/llama-server \
  --hf-repo raulvidis/Ling-3.0-flash-ROCmFP4-STRIX-MTP-GGUF:Q4_0 --alias Ling-3.0-Flash \
  --spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-n-min 0 --spec-draft-p-min 0.5 \
  --temp 0.6 --top-p 0.95 --top-k 20 --jinja \
  --cache-ram 4096 \
  -dev ROCm0 --webui \
  --host 0.0.0.0 --port 1337 \
  --gpu-layers 999 -fa on \
  --ctx-size 196608 --flash-attn on --kv-unified --cache-type-k q8_0 --cache-type-v q8_0 --cache-type-k-draft q8_0 --cache-type-v-draft q8_0 \
  -fit off -np 4 --no-mmap

Qwen 3.8 27b is a dense model, and so it’s pretty slow. The config i have feels fast enough for me. Ling 3.0 Flash is where the fun begins. It is, in my opinion, far better than Qwen 3.6 35b and is underrated.

I know that many don’t like the Artificial Intelligence Index, but i use it as a guide to find models worth my while. Ling 3.0 Tiny, Ling 3.0 Flash and Deepseek V4 Flash 0713 are particularly good for Strix Halo as they have a pretty small amount of active parameters.

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