We make tinygrad; sell tinybox for the GPU middle class. Our mission is to commoditize the petaflop.

San Diego
All products are now available to order! $45k for tinybox green with 5090s, and we have two variants of the tinybox pro v2, one with 5090s and one with RTX6000 Blackwells (that's a whopping 768 GB of RAM)
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the tiny corp retweeted
TIL that Linux isn't free cause I had to buy the computer to run it 😭 This is the biggest load of cope I have ever heard. I hope Opus gets smoked by DeepSeek v4 and the only people who continue to use closed source models are Windows users.
Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.
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To every researcher in US frontier labs. You used to publish. You justified not because you were shipping something millions of people love. Now you are shipping only to Trump's cronies. Consider your impact and legacy, you will be fine money wise. Monday is a good day to quit.
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Go work somewhere you can publish. Contribute to one of the great scientific revolutions. Remember why you got a PhD. It ends up public either way, it always does. But when you reflect from your deathbed you were on the right side and not a part of some small minded power grab.
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I can't believe how many people believe Anthropic's propaganda about distillation attacks. You can read how GLM-5 was trained in the paper. Maybe Claude was distilled from Chinese models? I haven't seen a paper from Anthropic, and accusations are often confessions.
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The US AI pay-to-play scam is so much more tolerable after switching to a locally hosted GLM-5.2. From the front page of HN, open weights will be the frontier this December. Sorry about your IPOs.
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the tiny corp retweeted
do whatever you have to do to acquire an exaflop of compute
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Someday we'll look back at when the government tried to restrict AI with too many parameters like when they tried to restrict encryption with too many bits. Though unlike last time, this time there will be consequences. The world will see China as the clear AI leader.
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Just like browsers, the model is the complement and will be given away free by the AI infra companies. China ramps up production while the US debates if B200s can be sold to Portugal. America was in the lead to be the AI infra provider for the world. That lead slips every day.
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RIP, no future in Mojo now. Qualcomm is not a good steward for open source software. Modular raised too much money, should have stayed leaner to have a chance at winning.
We’re excited to announce that Modular has entered an agreement to be acquired by @Qualcomm. The future of unified compute has never been stronger. Read the full announcement: modular.com/blog/qualcomm-to…
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the tiny corp retweeted
tinygradすきなのでtinygradのためのライブラリを作るなどをしたい。
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Today, if you were writing a bunch of kernels, what would you reach for? Raw CUDA? tile-lang? Triton? ThunderKittens?
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All day using GLM 5.2. Didn't miss much. First open model that passes the bar as a daily driver. Things are not going to be the same. Damn, now I want to buy some serious hardware.
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The new personal computer revolution is just beginning.
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I have on good authority that GLM 5.2 is running at 120 tok/s across two networked Blackwell tinyboxes. $150k and that setup can be yours, either 2x tinybox or 1x tinybox pro. Never pay the cloud again.
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Doing a Marxist analysis of tinygrad with GLM 5.2
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We live in a weird time of overhyping slop that will be forgotten about in weeks. Linux and Python are both from 1991. LLVM started as research project in 2000. We want to build the foundations of silicon life. Software that lives for 50 years. There's time to make it perfect.
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The tinygrad from the book has ~40 ops. ALU: 10 (base) + 10 (compound) movement: 6 (base) + 2 (STACK+BITCAST) + INDEX source: BUFFER/PARAM/CONST data: LOAD/STORE/AFTER -- how/when it moves lambda lift: CALL loops: RANGE/END scan: REDUCE -- needed? final: SINK -- group STORE
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Every UOp is a tuple: (op, srcs, arg?) shape, dtype, device, addrspace are recursively computed properties. This is what happens if you just keep refactoring. LLVM IR is good, but parts look like they locked it in before it was done. We value beauty over speed and features.
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STORE is the only op with any observable side effects. This IR spans from the PyTorch level graph down to the level of LLVM IR. It's notably not Turing complete.
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