/Tech5h ago

Former Meta PM Xiaoyin Qu predicts US and European enterprises will adopt Chinese AI models to self-host on private GPUs

Story Overview

A former Meta product manager argues that US and European companies will move toward Chinese AI models specifically so they can run them on their own GPUs, gaining direct control over data and the chance to fine-tune on internal datasets for competitive edges.

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#830

Original post

Rohan Paul@rohanpaul_ai#1257inTech

👀 This is from an ex-Meta PM.

Xiaoyin Qu@quxiaoyin

American and European enterprises will ditch OpenAI and anthropic and adopt Chinese models. Here’s why: 1. They can host Chinese models under their own GPUs so it’s still compliant and they would argue they have more control. 2. they will post train with their own data on top of Chinese models. That’s how they build data moat. 3. They will not trust anthropic who will retain their data at any time for “safety” concerns like how they did with Fable and then try to build the same thing like how anthropic did with healthcare and legal. 4. They need to justify their AI spend and ROI. The cure is a reliable America open source model but there is none. After all, if giving away all your data and AI control at the mercy of anthropic and OpenAI means you care about safety and compliance, you are outright stupid.

12:10 AM · Jun 29, 2026 · 98K Views

Policy Risk

Retention policies fuel enterprise hesitation

Anthropic now keeps prompts and outputs from its Mythos-class models for 30 days for safety reviews, overriding earlier zero-retention options and prompting questions about how much control organizations truly retain.

Open Question

Self-hosting remains an unproven bet

While open-weight Chinese models show strong benchmark results and lower costs, no large-scale Western enterprise migrations tied directly to these concerns have been confirmed yet.

Sentiment

Positive users agree enterprises will switch to Chinese AI models for data sovereignty and open-source hosting, while negative users dismiss the prediction as shilling or a Communist security threat.

Pos

44.4%

Neg

55.6%

18 comments with sentiment.

Cluster Engagement

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Posts from X

Most Activity

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Zephyr@zephyr_z9

"American and European enterprises will ditch OpenAI and anthropic and adopt Chinese models." I love fireworks, Nebius et al But they don't have the scale or compute required to support this

Xiaoyin Qu@quxiaoyin

@zephyr_z9 you can use Firework, Nebius and others

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RETWEETS4

Alexander Doria@Dorialexander

"They can host Chinese models under their own GPUs" => next blogpost will be how this isn’t as straightforward as that.

Xiaoyin Qu@quxiaoyin

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xlr8harder@xlr8harder

@Dorialexander Been thinking about this a lot, looking forward to reading your thoughts on it.

Alexander Doria@Dorialexander

"They can host Chinese models under their own GPUs" => next blogpost will be how this isn’t as straightforward as that.

1h15741

Juan Echeverria@JuanEcheverrria

@rohanpaul_ai That’s not going to happen. The U.S. government won’t allow it. They’ll ban any Chinese model that’s on par with Mythos/GPT-5.6.

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xjdr@_xjdr

@Dorialexander Can attest , it's not easy

Alexander Doria@Dorialexander

"They can host Chinese models under their own GPUs" => next blogpost will be how this isn’t as straightforward as that.

1h56380

wang yiwen@ThomasYiwen

@JuanEcheverrria @rohanpaul_ai how？the chinese models are free and open sourced

4h311

xjdr@_xjdr

@Dorialexander Don't get me wrong, it's doable. It's just a lot harder than it sounds and exponentially harder when you are serving it for more than one person

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Alexander Doria@Dorialexander

@_xjdr verbatim a point i've made…

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Alexander Doria@Dorialexander

@_xjdr If even you say it…

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em@NoemiTitarenco

@rohanpaul_ai And shes right

4h521

Einar Altsson@EinarAltsson

@Dorialexander @_xjdr Okay, GLM 5 was not yet tackled, so can't comment on that one. Only Qwen 397B for developers on 4 GPUs so far, but no issues there. Maybe just takes some time for vLLM/SGLang to catch up here..

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Alexander Doria@Dorialexander

@xlr8harder Incidentally also matters to run this on HPC for synth…

1h362

Alexander Doria@Dorialexander

@synquid @xlr8harder biggest one is actually tool exec latency/orchestration (though might have a solution, just very unconventional)

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Alexander Doria@Dorialexander

@_xjdr No but that's really the key thing: average large co might have the compute, just not the skills.

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Alexander Doria@Dorialexander

@EinarAltsson @_xjdr Depends on the use case but the one most people would look GLM for (local claude code) is not really solved…

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Rasmus@synquid

@Dorialexander @xlr8harder Interested in your challenges here, too slow inference?

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Einar Altsson@EinarAltsson

@Dorialexander @_xjdr Really looking forward to the blog post, because so far I did not have any serious issues serving models on a single node for a medium sized company. Efficiency is not an issue with B2B, really 🤷🏻‍♂️

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xlr8harder@xlr8harder

@Dorialexander HPC solution for this is much harder. Slurm is not shaped well for this workflow. I might have a useful tool for that environment, will DM you.

1h25

Flux of the Archive@flux_of_archive

@ThomasYiwen @JuanEcheverrria @rohanpaul_ai They almost banned deepseek like a year ago, making it a crime in America to use it. I think the real motivation is to keep the AI models more distinct from one another, rather than having them be effectively siblings due to cross-model distillation.

4h17

Alexander Doria@Dorialexander

@EinarAltsson @_xjdr ok not bad. and you're running it for coding? with some parallel session management?

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