What this is
10 hours, 10 million tokens, a single RTX 3090 graphics card — that's the test data a developer posted last week on Reddit's r/LocalLLaMA (a community of enthusiasts running open-source large models locally): pairing Alibaba Tongyi's Qwen 3.8 27B model with DeepSeek's open-source scheduling framework (Harness — the "operating system layer" that lets a model keep executing long tasks without losing its way), they got a coding agent that ran 10 hours without derailing.
Speed fluctuates between 37–60 tokens/sec depending on context length. The trade-off: the model "thinks deeply" — in one case it spun for 20 minutes before writing a single line. The user also admitted that 27B is a dense model (every inference has to compute all parameters), so running it as an agent all day isn't realistic. He's waiting for Alibaba to ship a 35B MoE version (Mixture of Experts — only activates a subset of parameters, smaller footprint, cheaper inference).
How the industry sees it
We note that what's actually worth paying attention to isn't "how strong the model is" but that the scheduling framework is decoupling from the model. When harness-layer code like this can be independently open-sourced — unbound to specific model weights — the AI industry is entering a modular phase reminiscent of early Linux: Qwen's brain, DeepSeek's scheduling, and your own UI are becoming a plug-and-play combination punch.
But the counter-argument belongs on the table: this is one Reddit user's personal benchmark, unverified by third-party evaluations like SWE-Bench or AgentBench. "10 hours without failure" is exactly the metric enterprises care most about — and also the promise most likely to crack once sample sizes grow. 37 tokens/sec means a typical coding task takes minutes to tens of minutes. Individual developers can tolerate that; production lines are a different story.
What it means for regular people
For enterprise IT: A single RTX 3090 at roughly 15,000 RMB (~$2,100) plus free open-source models can already run a coding agent that holds steady for several hours. That's a direct tailwind for the "compute bill" of small and mid-sized software teams — no longer paying U.S. APIs by the token for every line of code.
For individual careers: For now it only matters to technical roles willing to wait and willing to tinker. "Can tune a local model + accepts being 10x slower" is becoming a new skill threshold — most white-collar workers don't need it yet.
For the consumer market: No short-term impact. Once consumer hardware drops another tier and local models shrink another 50% in size, "running an AI assistant at home" can plausibly reach ordinary households.