I Asked the Same Question to 7 Local LLMs — Speed and Intelligence Didn't Line Up: DGX Spark Benchmarks
Originally published on my Substack . I'm a Microsoft MVP based in Japan, writing in English about the AI agent systems I actually run in production. Local AI models keep multiplying. But comparing numbers on model cards alone doesn't tell you which one to actually use. Does a higher parameter count mean smarter? Does MoE mean faster? If a model is popular on AI Arena, is it good for my own work?…
I conducted a test using the same query across seven major language models (LLMs) running on a single NVIDIA DGX Spark system. The goal was to evaluate not just the speed but also the quality and reliability of the generated answers. Among the models tested, Qwen3.5 35B was the quickest, completing the task in 1.49 seconds. However, its answer contained a risky claim about zero risk of confidential data leakage, which is not suitable for business use.
On the other hand, Qwen3.6 35B-A3B, which took slightly longer at 2.12 seconds, provided a more practical answer that addressed the risk of data leakage and required specialized knowledge for setup. Ultimately, while speed is an important factor, it did not consistently correlate with the reliability or suitability of the answers for business applications.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
