Fabian G. Williams aka Fabs

Fabian G. Williams

Principal Product Manager, Microsoft Subscribe to my YouTube.

Two Agents, One Local Model: Do They Run in Parallel, or Take Turns? I Measured It.

A reader asked what happens if I run two local coding agents against the same Qwen 3.8 model on one Mac at the same time. I thought I knew the answer. I was wrong. So I read the server source, wrote a barrier-synchronized load driver to remove the human-ordering bias, and measured it. Batching is real up to 32 wide, but it is not free, and three innocent-looking choices collapse it back to a single lane.

Fabian Williams

9-Minute Read

A line chart showing aggregate throughput rising with concurrent agents while per-agent decode rate falls, on one local MLX model

A reader on Reddit asked me a sharp question about my last post. I had trialed a second local coding agent, Hermes, pointed at the same local Qwen 3.8 model my other agent already uses. His question was simple. Did I ever run both agents at the same time, two separate harnesses hammering one model on one Mac at once. And what about Hermes spawning its own sub-agents against that same endpoint. Is any of that predictable.

I Swapped My Local Coding Model Overnight From a Hotel. The Agent Graded the Upgrade Itself.

Local models are how I run my community work, my volunteer projects, my side hustles, and my musings, for two reasons: cost and keeping client data away from the labs. A new version dropped, so I upgraded it overnight from a hotel, additively, without touching the old one. Then I handed the new model the reviews and let my OpenCode agent test its own upgrade. It stood up a throwaway server, probed itself, and found three config gaps quietly throttling it. Here is the journey, in tables.

Fabian Williams

8-Minute Read

Activity Monitor showing the M3 Max GPU pinned at 92 percent while an OpenCode agent runs probe requests against a throwaway Qwen3.8 test server on port 8082

Local models are how I run my community work, my volunteer projects, my side hustles, and my musings. Two reasons, both simple:

Replacing gpt-oss:120b With Qwen3.6 on a MacBook Pro: A Two-Day Local Model Benchmark

Two days benchmarking three Qwen3.6 variants against gpt-oss:120b on an M3 Max. A 21 GB coding-tuned model ran an OpenClaw-shaped research-brief workload 10x faster than gpt-oss — fast enough to seriously consider moving the work off SaaS frontier APIs. Plus the silent-hallucination trap I almost shipped through.

Fabian Williams

14-Minute Read

Bar chart comparing wall time of four local models on a structured-output benchmark

I spent two days benchmarking three Qwen3.6 variants against gpt-oss:120b on my MacBook Pro M3 Max. The shocking result: a 21 GB coding-tuned model ran an OpenClaw-shaped research-brief workload that I use for the non profit MACONA.org in 6 seconds — 10x faster than gpt-oss:120b on the same prompt. Fast enough that I now have reasonable confidence I could move this kind of work off the SaaS-hosted frontier models I have been paying for and onto local hardware on my dev machine. The deeper…

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