Qwen Meets FLock: Centralized/Decentralized Hybrid

FLock teams up with Qwen to combine a leading Chinese LLM with decentralized AI training, tackling data privacy and broadening enterprise adoption.
Digital generated image of neon multi coloured spheres inside glass human figures. Concept of artificial intelligence, relationship and new fintech (GettyImages).
Digital generated image of neon multi coloured spheres inside glass human figures. Concept of artificial intelligence, relationship and new fintech (GettyImages).
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Jonathan Morgan·Stocktwits
Updated Jul 02, 2025   |   8:31 PM GMT-04
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FLock (FLOCK) just announced a partnership with Qwen, one of China’s hottest open-source LLMs. 

If you’ve never heard of Qwen, think of it as the ChatGPT of China - except ChatGPT is blocked there, so Qwen soared in popularity, outrunning Mistral or DeepSeek-R1 in multiple benchmarks. 

Alibaba Cloud develops Qwen, meaning it’s got real muscle behind it.

Now, FLock’s hooking up its Decentralized AI Agent model into Alibaba Cloud. For a while, folks doubted “DeAI” (Decentralized AI) had any tangible real-world use. FLock wants to prove them wrong. 

They say that combining Qwen’s mainstream AI popularity with their distributed ledger approach can help solve data privacy and sovereignty concerns in AI model training. It’s all about letting smaller data providers share info without risking leaks or exploitation.

FLock calls the synergy “true protocol-level integration,” bridging a centralized star (Qwen) with a decentralized approach (FLock). They’ll collaborate on specific domain-focused models - like healthcare or finance—plus more general AI. 

The real kicker is that Alibaba’s resources might help FLock go global. It’s certainly easier to get traction when a cloud giant has your back. This also hints at a bigger shift: big AI vendors are starting to take notice of decentralized solutions. 

FLock expects to unify the best of both worlds: centrally-developed LLM power and a decentralized training environment that ensures data privacy. If it works, it could bring more private or enterprise data sets onto the training scene, fueling better custom models.

Of course, the talk of “DeAI synergy” might still be hype if results don’t materialize. But if Qwen keeps climbing benchmarks, and FLock integrates seamlessly, we might see new cutting-edge AI that’s both high-performance and privacy-respecting. 

If that impresses real businesses, well, DeAI might get that real-world respect it’s been missing. But let's see what happens when scaling issues starts to press in. 

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