I've used all the popular models, and I run a 48-workstation mini data center with various GPUs, running open source models for a variety of tasks. I'm here to tell you that, without question, China's open source models are going to crush the U.S. AI industry, leading sooner or later to a stock valuation collapse as it sinks in that nobody will consistently pay for AI inference from U.S. companies when they can receive nearly the exact same level of capability and intelligence from China's models at either ZERO cost (download the models and run them yourself), or at a tiny fraction of the cost of U.S. models (typically from 1/50th to 1/100th the cost of U.S. models).

In response to this, U.S. AI companies are dramatically raising their prices and switching customer plans to per-token pricing in order to maximize their own revenues. This is only going to work against them and will lead to a mass exodus away from U.S. AI companies. In fact, that exodus has already begun (see below).

But there's something else that's critical to understand here.

You can easily download the open weights of China's frontier AI models like DeepSeek V4, GLM 2.5, etc. But unless you have access to very expensive server hardware costing hundreds of thousands of dollars, you can't run those models locally.

256GB AI Inference Systems Are Coming to the Desktop

In 2027, however, that picture changes dramatically. AMD has announced it will launch "Gorgon Halo" (a revamped Strix Halo) with 192GB of unified memory (160GB can be used as VRAM).

Apple has its "Apple Mac Studio (M3 Ultra) with up to 512GB of unified memory (at 819GB/s memory bandwidth, which is respectable). Unfortunately, due to the global RAM shortage, Apple pulled the 512GB version off the market and now sells a max memory configuration of 256GB (which is still a lot for unified RAM).

Even more interestingly, in 2027, AMD will be rolling out "Medusa Halo" with a rumored 256GB of VRAM and over 512GB/s memory bandwidth). This one will run on LPDDR6 high bandwidth memory, which is scarce and expensive, so these systems won't be cheap, but they'll still be a fraction of the cost of today's Nvidia server GPU setups.