Talus is an open-source (MIT) inference engine for Strix Halo (Max+ 395/128G). The engine is a fork of Strata, a C++/HIP engine by Niko1221. We ported Strata’s host and server to Rust, tried a few other popular models, and re-tuned the GPU kernels, which come from Strata and, through it, from ggml/llama.cpp, for gfx1151. It serves one model at a time through an OpenAI-compatible API. A terminal UI switches models and presets, and explains trade offs when changing settings.
Why?
I started chasing the big MoE models that I could fit on this laptop, which spiraled into forking everycode base I could find for performance.
The Goods:
HP ZBook Ultra G1a, Ryzen AI Max+ 395, 128 GB, firmware-capped at 70 W, headless Linux 7.0
Qwen3.8-27B, Qwen3.8-Flash-Next, GLM-5.3-Flash, MiMo-V2.6-Flash, Laguna-S-2.1. Numbers:
(tok/s, end to end through the server, on an HP ZBook Ultra G1a capped at 70 W)
How we got here:
What mattered for gfx1151:
Honest Comparisons (same laptop, measured by us)
Credits:
Strata - (Niko1221): the base. ggml / llama.cpp: kernels and formats.
The model teams: Qwen, Z.ai, Xiaomi MiMo, poolside.
Unsloth: the GGUF quants
z-lab: the DFlash drafters.
Gufo: the long-copy drafting idea.
AMD ROCm.
What’s next:
Talus is tuned and tested only on Strix Halo today. The code keeps per-GPU tables amd multi-device hooks, so other AMD GPUs (RDNA4) and multi-box setups may come later. A built-in benchmark and validation tool (quality suites plus SWE-bench with anti-cheating) is coming in the next release.
Github: nsbradley88/talus-public HuggingFace: j2ipper