This week a Reddit developer ran Alibaba's Qwen 27B open-source LLM on two NVIDIA RTX 3060 gaming cards—at a hardware cost of under ¥4,000—hitting a steady generation speed of 45-50 tokens/second (a token can be roughly understood as a "character"). The community confirmed it's sufficient for daily use. This is historic: four years ago, a graphics card at this price point couldn't run a 27-billion-parameter Chinese LLM. We note that the leading edge of this hardware-threshold decline is being driven by domestic models.
What this is
User jacek2023 posted real-world test results in the LocalLLaMA subreddit: using two RTX 3060s (gaming cards launched in 2021, currently ¥1,500-2,000 each on the secondhand market) to run Qwen 27B (open-source Chinese LLM, 27 billion parameters), achieving 45-50 tokens/sec generation speed, with two concurrent 5,000-character prompts also performing adequately. Self-assessment from the developer: "Good enough for daily agent conversations."
What we want to highlight is the timeline: four years ago, ¥2,000 graphics cards couldn't do this kind of work; now they can. The gap isn't in the GPUs—it's that model quantization (compressing models to use less space) and inference optimization (making models run faster on lower-end hardware) have hit an inflection point.
Industry view
From the supporters' angle: local AI's value has never been about speed—it's about "data never leaves the premises." Two RTX 3060s plus an old workstation chassis, and small-to-medium companies can hand internal documents and customer data to a local LLM, uploading nothing to the cloud—hitting the budget-constrained but data-sensitive customer segment.
We can't omit the opposing voices. First is speed: 45-50 tokens/sec is on the slow side for formal enterprise workloads; with the RTX 3060's VRAM ceiling, agents (AI programs that can autonomously run multi-step tasks) hit walls on long conversations and long documents. Second is supply chain: the RTX 3060 has been discontinued, and secondhand prices are being pushed up by the AI boom. Third is talent: what's holding back SMBs from local AI isn't usually hardware—it's the lack of engineers who understand deployment and operations.
Impact on regular people
For enterprise IT: under ¥5,000 in hardware can build a local environment capable of running a Chinese LLM—an order-of-magnitude drop from previous tens-of-thousands-yuan solutions; electricity bills and operations labor are part of the real total cost—hardware is just the entry ticket.
For working professionals: white-collar workers willing to tinker can buy two secondhand GPUs and run local AI themselves to handle sensitive data that can't be uploaded; non-technical colleagues wanting equivalent capability can still only rely on cloud services or company-wide procurement.
For the consumer market: local AI deployment demand is rising, and the secondhand GPU and small workstation market may enter a price-increase cycle; ordinary gamers' cost of buying gaming cards will likely be pushed up by this AI capital wave.