Swift 1.5在Strix Halo上的基准测试
r/LocalLLaMA一名用户在一台拥有128 GB内存、功耗限制为70 W的Asus ROG Flow Z13上，将UkisAI的Qwen3.8-Flash-Next微调版Swift 1.5与Unsloth的量化版本进行了比较，测试覆盖Halogen、gufo和llama.cpp。在11项Pi编程智能体任务中，Swift消耗了5.6万至6.3万输出令牌，而Qwen3.8-Flash-Next消耗了7.5万至9.9万；gufo运行Swift耗时24分钟，Unsloth UD-Q4_K_XL则为33分钟。不过，数学和LiveCodeBench结果表现不一，作者也提醒所有测试仅运行一次，一两道题的差异可能是偶然因素。
该比较提供了Swift 1.5在本地推理中减少编程智能体令牌消耗和运行时间的实测证据，但单次运行和特定硬件条件限制了结论的确定性。
Here I am with another benchmark/comparison. I was curious about the efficiency claims of Swift 1.5, UkisAI's fine-tune of Qwen3.8-Flash-Next that thinks for a shorter time. UkisAI says Swift uses 63% fewer thinking tokens with under 1% accuracy loss.
I compared it with my current setup (Halogen) and Unsloth's UD-Q4_K_XL on my Asus ROG Flow Z13 (128 GB, 70 W). I ran the models on Halogen, gufo and llama.cpp, all with MTP. Since gufo can't load any of UkisAI's published 4-bit Swift GGUFs, and didnt have an XL version, I requantized their Swift Q8_0 to the same per-tensor types as Unsloth's UD-Q4_K_XL, with UkisAI's imatrix. It is even the same size as Unsloth's UD-Q4_K_XL (111.3 GB).
My main use is coding agents, so I also ran 11 coding tasks through Pi and threw in some math problems and LCB tasks to compare quality and speed. Speeds at 32k filled prompt. I got very interesting results but out of 1 run so take them with a grain of salt.
Here are the results sorted by total score and then total time.
#Swift 1.5#Qwen3.8-Flash-Next#UkisAI#Unsloth#Halogen#gufo#llama.cpp#Pi#Asus ROG Flow Z13