# Qwen 3 + KTransformers 0.3 (+AMX) = AI Workstation/PC
Following DeepSeek-V3/R1, LLaMa-4, and Kimi-VL, Qwen has also released an impressive MoE model—undoubtedly, this year belongs to MoE. As a low-barrier inference system for running MoE models in local heterogeneous environments, KTransformers naturally joins the party. Thanks to the support of the Qwen team, we completed Day 0 support for the entire Qwen 3 series of MoE models. At the same time, we took this opportunity to open-source the long-awaited preliminary version of our AMX high-performance operators (BF16, Int8; an int4 variant is coming soon), officially advancing to version 0.3.
What excites me most about Qwen3MoE is that, unlike the 671 B “giant” model, its two configurations—235B-A22 and 30B-A3B—hit the performance sweet spots for both local workstations and consumer-grade PCs. Accordingly, we ran benchmarks in two typical setups:
What excites me most about Qwen3MoE is that, unlike the 671 B “giant” model, its two configurations: 235B-A22 and 30B-A3B, **hit the performance sweet spots for both local workstations and consumer-grade PCs**. Accordingly, we ran benchmarks in two typical setups:
You can see that, thanks to the AMX instruction optimizations, we achieve up to 347 tokens/s prefill performance in the workstation scenario. On consumer-grade CPUs, we’re able to run the large model (235B-A22) and deliver smooth performance on the smaller 30B-A3B. Even in terms of resource overhead, it appears that a high-end gaming laptop can handle 30B-A3B smoothly. After talking about the concept of AIPC for so long, we can finally see its feasibility.
