Oliver Frankfurth

Next step: see what's left to extract at this low-concurrency profile (2-3 streams) — most optimizations are designed for high-throughput production, not internal setups with thin load. Anyone running 35B-MoE at low concurrency with tips?

We run 2-3 concurrent requests, no 128-stream production load. The boost still shows up in daily use. Lesson: „defaulting conservative" can kill the very performance path you're trying to use.

Spent today tinkering with model optimization on our DGX Spark setup. Others reported solid results with specialized Qwen3.6-35B-A3B variants — wanted to see how much of that actually shows up under my workload. 🧵

Two machines on a desk. Cost less than one year of ChatGPT Enterprise for a 10-person team. Running Nemotron 120B (MoE, 12B active params) for reasoning and agent orchestration. Qwen 3.6 35B (MoE, 3B active) at 43 tok/s for fast tasks. Both on NVIDIA DGX Spark. 128GB unified memory. FP8 KV cache. 128K context. 8 concurrent requests. Requests get prioritized — urgent first, batch jobs when there's room. Claude? We use it too. But only when it matters: coding, architecture, complex reasoning. 90% of daily workflows run locally. No subscription, no API costs. The key: we control what goes out. Customer data, financials, strategy — stays local. Always. When we use frontier models, we decide what enters the context. Nothing leaves the office by accident. Can local models compete with GPT-5? No. They don't need to. Most business tasks need reliability, not benchmarks.

I have a problem with AI content. Not with the technology. With what most people do with it. Generic text that sounds like nothing. Interchangeable. Empty. We serve 40,000+ clients across three websites. Small team, lots of content. Of course we use AI. But with one rule: every text gets reviewed, edited, and approved by a human before it goes live. AI delivers the draft. Quality comes from us. "More content" is not the goal. Better content is.

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