@zhixianio: After receiving the new machine, I began an 'ascetic' practice of forcing myself to use local models for common tasks. I thought it would be painful, but both speed and quality greatly exceeded my expectations: Model: Qwen3.6-35B-A3B-oQ6-fp16-mtp, Running: oMLX, with N…
Summary
The author uses the Qwen3.6-35B-A3B model and oMLX tool on the new local machine for daily tasks, finding that both speed and quality far exceed expectations, even outperforming remote LLMs in PA and coding scenarios, demonstrating a significant improvement in on-device AI capabilities.
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Cached at: 06/03/26, 07:46 AM
After the new machine arrived these past two days, I began an ascetic-like practice of forcing myself to use local models to complete common tasks.
Expected it to be extremely painful, but to my surprise, both the speed and quality far exceeded my expectations:
Model: Qwen3.6-35B-A3B-oQ6-fp16-mtp Running: oMLX, Native MTP enabled, Thinking disabled, CTX 128K
These days, whether in the PA scenario (OpenClaw) or the Coding scenario (PI-Mono), the response speed is faster than remote LLMs, the intelligence is on-point, and it can even help me think and do one more step. The native multimodal capabilities under PA are even more satisfying to use than DSV4 Pro.
I never expected that the capabilities of on-device models have reached this level.
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