What This Is
This week, a Reddit user running local LLMs on 16GB VRAM + 128GB RAM reached a judgment that caught our attention: the "co-think as you write" form sitting between AI autocomplete and Agents (where AI autonomously plans multi-step tasks and operates software) — nobody is seriously building it.
He tested Qwen's (Alibaba's open-source Chinese LLM) 35B version, decoding (text generation) at 40-60 tokens/sec; he tested DeepSeek's Flash version with Q2 quantization (extreme low-precision compression to save VRAM), which only managed 10 tokens/sec. His daily work still uses OpenAI's Codex (cloud-based coding assistant, $20/month) — the local setup is just an "experimental toy."
This user's value isn't in the hardware config — it's in providing a concrete test that gives the industry a sample of the form that's been skipped.
Industry View
Supporters argue the gap really exists. Local runs are fast and private (data stays on the machine), especially suited for programmers. Chinese open-source models like Qwen have improved in quality, and the hardware threshold has dropped to a 10,000 RMB gaming PC, making local AI much more playable. We've also noticed that cloud vendors and the open-source community are both exploring the "lightweight assistance" direction.
The opposing view is more worth hearing. First, 40-60 tokens/sec for coding is still slow — humans write code by "thinking then typing," and waiting half a second for AI to spit out one line breaks the flow. Second, this user's daily work still uses cloud-based Codex, with local being just an "experimental toy" — meaning enterprise cloud subscriptions are where the real money is. Third, the "middle ground" is more likely enthusiast self-indulgence — neither as light as autocomplete nor as automatic as Agents. In our view, it's more likely to become a consumer product than a productivity tool.
Impact on Regular People
For enterprise IT: Local deployment's hardware threshold has dropped to 10,000 RMB gaming PC level, and data-sensitive industries (healthcare, government, finance) can evaluate pilots — but moving from cloud requires a clear accounting of human operations costs.
For individual professionals: Most knowledge workers don't need local setups — cloud assistants (ChatGPT, Claude, ERNIE Bot, Doubao) are fast and good enough. Unless you're writing code or handling sensitive data, tinkering with local is a poor cost-performance choice.
For the consumer market: 16GB VRAM consumer GPUs (like the 5060 Ti) are being bought up by local AI enthusiasts — a new growth segment for PC hardware — but the scale is still very early, and mainstream users won't follow in the short term.