What this is
This week, a hands-on benchmark post appeared on Reddit's local AI deployment community r/LocalLLaMA: user Legitimate_Hat_7852 built a 4-card cluster using NVIDIA's DGX Spark (a compact AI inference workstation capable of running large models on a single machine), and simultaneously deployed three of China's top open-source models (LLMs with public code and weights, free to download and run) — DeepSeek V4 0731, Qwen 3.8 Flash, and GLM 5.3 Flash.
GLM 5.3 Flash was eliminated: its outputs were excessively verbose, and its dual-card speed clocked only 22 tok/s (22 tokens per second — far below the 60+ needed for fluid reading). The final setup: DeepSeek runs the main tasks (planning and building), Qwen runs the subtasks (exploration and delegation — breaking work down and dispatching it to AI assistants).
Industry view
This is a tinkerer's notes from one individual user, but it surfaces three signals worth flagging from the editorial desk:
First, the gap between China's top open-source models is shifting from "does it work" to "style difference." A year ago, everyone was comparing benchmark scores. Now the race is about which model fits your workflow best. Once capabilities converge, "how smoothly does it work" becomes the new battleground.
Second, multi-model division of labor is the clearest small trend in AI deployment for the second half of 2025. The industry calls it multi-model orchestration: instead of searching for one universal model, you combine models based on speed, cost, and strengths. This path is more realistic than "waiting for a super-model."
The risk worth flagging: this kind of deployment is heavily hardware-dependent. A single DGX Spark starts at 10,000 RMB, and a 4-card cluster remains a luxury for SMEs. The original poster is a tech power-user; their workflow cannot be directly replicated by ordinary companies. So-called "multi-model division of labor" is still a toy for teams with R&D capability.
Impact on regular people
For enterprise IT: the selection logic has to change. Stop asking "which model is strongest," and start asking "can my workload be split into two categories — one needing accuracy, one needing speed?" Multi-model combinations may be more cost-effective and more resilient than single-vendor procurement.
For individual professionals: you don't need to worry about deployment yet. But vague claims like "I know how to make AI do work" are losing value. People who can decompose tasks and pick the right tool are pulling ahead; everyone else is just "someone who knows how to use ChatGPT."
For consumer markets: no immediate impact. No matter how cheap open-source models get, the deployment barrier (10,000+ RMB in hardware plus technical skill) keeps them out of consumer reach. But cloud providers will use this to push prices down — future consumer-facing AI features will likely be cheaper and faster.