Below are the benchmark results for running Qwen3.8-Flash-Next on Strix Halo using the Vulkan backend of llama.cpp, combined with MTP model.

Hardware

Software

Kernel Boot Parameters (Excerpt) amdgpu.gttsize=126976 amdgpu.noretry=0 ttm.pages_limit=28835840 ttm.page_pool_size=14417920 iommu=off

llama.cpp

Using the fork by Laurent Zuijdwijk:

git clone https://github.com/LaurentZuijdwijk/llama.cpp cd llama.cpp && git checkout vulkan/qwen4exp-rocmfpx 

Models

• Main: Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64 (AtomicChat)
• MTP Draft: Qwen3.8-Flash-Next-MTP-Q4_K_M.gguf (dzannotti)

Launch Command

./build/bin/llama-server \ --host 0.0.0.0 --port 8080 \ --model ./models/Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64/Qwen3.8-Flash-Next-AD-5.00bpw-Q5_K_M-M64-00001-of-00033.gguf \ -c 262144 --n-predict 32768 \ -t 2 --threads-batch 8 \ -ngl 999 --parallel 1 \ -b 8192 --ubatch-size 512 \ --load-mode mlock \ -fa on -cb \ -ctk f16 -ctv f16 \ --cache-reuse 1024 \ --jinja --reasoning on --reasoning-preserve \ --cache-prompt \ --chat-template-kwargs '{"reasoning_effort":"medium"}' \ -md ./models/Qwen3.8-Flash-Next-MTP-Q4_K_M.gguf \ --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-p-min 0.75 

Benchmark Results ($n=85$)

Observations

When compared with Qwen3.8-27B, the quality of output for architectural and design tasks (such as OpenSpec proposals) appears noticeably superior. Depending on the instructions provided, it occasionally strays while attempting to "improve" the output, yet overall the performance is entirely satisfactory.

I generated the Japanese text using Claude(Opus4.6) and then translated it using Gemini(Flash 3.6).