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this post was submitted on 22 May 2026
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The Chinese AI labs are really trying to pop the bubble, too.
How?
Well lemme ask you this. What if models 80-90% as good as Claude, with weights just thrown out there for any provider (or homelab) to host, flood the market? What if they're so dirt cheap to run, they're almost free, and don't even need Nvidia GPUs? What they need fewer resources to run with each update, instead of more?
...What if this already happened, and Big Tech is maddly lobbying to ban/censor them before people realize it, and that the "infinite scaling" thing is a big fat lie?
That's the state of things.
It turns out that off-shoring your economy to a political rival is a really dumb thing for a capitalist to do.
If you offshore your economy, what prevents the recipient from becoming a rival?
We've offshored to all the BRICS countries and they've all become our rivals was a result
Well a funny thing about off-shoring your economy is that it really just means exploiting people in countries that can't stand up to your imperial might. So it inevitably creates enemies. Now you have no economy at home AND the rest of the world hates you! Double stupidity!
but also necessary and that's the beautiful contradiction of capitalism that will cause it's inevitable downfall
(Exact source unkown)
Or:
"Just keep making and selling them more rope, eventually they'll hang themselves with it."
But, but, this quarter profits.
I wish, I wish we would bring out the guillatine for these greedy treasonous capitalist fucks.
We've lost so much because of them
Yep, the Chinese models are already up 10 times cheaper and now that Anthropic, OpenAI, and Google, all are increasing prices up to 10 more for models like Opus, it will make Chinese models anywhere from 50 to 100 times cheaper.
American corps. are betting that since people have their workflow already established they won't switch to other providers, but that's not the case. There's already a mass move to Chinese models.
In a way it has actually.
Deepseek was big because not only did they publish the full model for everyone to use, but the MoE structure significantly brought down the hardware requirements in terms of processing power. As long as you have enough VRAM, you can run it on older hardware with no need for the latest Nvidia stuff.
Now they got v4 which many have found to be within a 10% margin of Claude and ChatGPT.
On top of that, China has cheapo VRAM GPUs available or soon to be released, like the MTT S80. Yeah it sucks as a Graphics card because the chip is behind, but you get 16Gb of GDDR6 for much cheaper than anything else.
But its not a conspiracy to fight China. The infinite scaling was just Nvidia solidifying themselves as the monopoly because they want all AI infrastructure to be dependent on them, which is why they still illegally export to China, despite an export ban attempting to reduce their potential competition.
Moore Threads (MTT) already has their own CUDA like system called MUSA, and I'm sure they'll be happy to put in proper hardware support for new stuff like Bf16 and FP8/4. It'll take a few years, but eventually China will catch up to the point where Nvidia gets shanked by cheaper hardware.
MTT is just a pipe dream, last I checked. But Deepseek is actively being served, in mixed FP8/FP4, on racks of Huawei accelerators.
I believe Baidu trained a model on them, too. But most training (like Deepseek’s) is still done on CUDA.
…Also, be careful equating this stuff with any kind of “consumer friendly” hardware you or I could buy. That’s less likely. The Huawei accelerators (and other local Chinese hardware experiments) are geared towards huge servers serving requests in parallel.
Wasn't there development of a linux translation layer for CUDA workloads to run on AMD GPUs? I haven't heard about it in a while, but I'd imagine that'd help the situation.
Hyper scaling was always about cornering the computer market, It was never about providing us some vastly new and superior service.
They should be strung up. And middle management needs to return to fucking school.
It’s like Kyle Kulinski said “I’m starting to understand re-education camps now”
Agreed. I am not longer paying token fees as I am running QWEN 3.6 27B MTP on my 4090 GPU and it is as good and as fast as the frontier models for agentic coding.
Same. I'm running Qwen3.6-35B-A3B-FP8 (Qwen3.6-35B-A3B-UD-IQ4_XS.gguf) via the turboquant fork of llama.cpp with a few tweaked memory settings, and I get like 40 tokens / second -- nothing that required special insight on my part just following the instructions I saw on a youtube video I found via !LocalLLaMA@sh.itjust.works and asking claude to help me through the installation.
AI has no economic moat. There's nothing stopping anyone from running LLMs locally.
I just updated my setup from LMStudio to llama.cpp with the new QWEN 3.6 27B MTP model and I am getting 80-112 tokens/second, 90 average which is just shocking to me. I am on a 4090 with a context Window of 64k. It hardly use cloud AI anymore as I rarely need more than 64k if I ensure my first prompt is written like a design document. Multiple prompts are not great so I often just figure out where my initial prompt went wrong, adjust and try again in a fresh session. Way faster this way too. It has really worked out well for me as I am getting just as much done locally for free as I was with hundreds of dollar a month on cloud AI. I am still shocked and grateful it flowed this way.
What do you run it on?
https://www.amazon.com/dp/B0BV8H8HVD with linux mint installed.
What's the rest of your stack look like?
I am using llamma.cpp with QWEN 3.6 27B MTP, with a 64k context window on a 4090 that OpenCode talks to and then it in term talks to the Unity Game engine via MCP. Getting 80/112 tokens/second work 90 average which is shocking to me as it really does feel as fast as cloud AI (well faster for me as I am in Vietnam and round trips to US data centers really adds up in a session). The only really issue is you pretty much have to one shot prompts as follow up prompts will easily go over the context window size. If I cannot one shot prompts them use cloud AI both that is very rare for my use case. Maybe 1 in 50 or so and only when the tasks touches a lot of large scripts and scenes.