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Introducing The AI CUDA Engineer: An agentic AI system that automates the production of highly optimized CUDA kernels. The AI CUDA Engineer can produce highly optimized CUDA kernels, reaching 10-100x speedup over common machine learning operations in PyTorch. Our system is also able to produce highly optimized CUDA kernels...

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Sakana AI

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1,160,266 görüntüleme • 1 yıl önce •via X (Twitter)

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vittorio1 yıl önce

japan bros are back again

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Globant1 yıl önce

🚀 Agentic AI Systems are changing what AI can do by having the power to act independently. Unlike traditional AI, which needs constant human supervision, these systems can operate more autonomously. Learn how this shift is leading to smarter solutions that can transform industries.➡️ #TechTrends2025

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ludwig1 yıl önce

I’m going to sleep if I wake up to this having 1M+ views I will read the paper tomorrow morning else pls give me a vibe check chat

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Bing Xu1 yıl önce

I quickly take a look of their report on phone, there are a few misleading parts: 1. Torch C++ code is not CUDA kernel, it is calling CUDNN under hood. 2. The highlighted example Conv3D GroupNorm, conv code is not generated at all. The speedup doesn’t make sense if numerical is wrong. 3. It claims wmma can be faster than PyTorch (CUBLAS), is definitely wrong. Probably benchmark error.

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main1 yıl önce

isn't there clearly something wrong with level_1->15_Matmul_for_lower_triangular_matrices? claimed 152.9x speedup for the kernel on the left over the code on the right. really?

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Viraat1 yıl önce

Hey - wondering if you all are only working with large enterprises right now. If not, we’d love to chat! This would be extremely useful to us - we’re building low-bit models to run efficiently on Jetsons. Generating optimized CUDA code for these would be a game-changer!

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aizk ✡️1 yıl önce

A slow 14 seconds in AI developments

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Dan Mac1 yıl önce

guys seriously I can't take it anymore need to slow down

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Kristof1 yıl önce

Japan is back

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Dr Futuro - e/acc1 yıl önce

Wow, Japan is back! 🇯🇵

Benzer Videolar

🎉 Stoked to share The AI CUDA Engineer 👷 - our end-to-end approach for automating the design and optimization of CUDA Kernels using agentic systems. Blog 📰: Paper 📜: WebUI 📈: Dataset 💽: Awesome team work done with Aaditya Prasad 🇺🇸, Suuun, Maxence Faldor, Yujin Tang, hardmaru 🤗
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🎉 Stoked to share The AI CUDA Engineer 👷 - our end-to-end approach for automating the design and optimization of CUDA Kernels using agentic systems. Blog 📰: Paper 📜: WebUI 📈: Dataset 💽: Awesome team work done with Aaditya Prasad 🇺🇸, Suuun, Maxence Faldor, Yujin Tang, hardmaru 🤗

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AI that responds in under 100ms doesn't happen by accident. Together AI VP of Kernels Dan Fu shares how they use NVIDIA CUDA, TensorRT-LLM, Dynamo and Together ATLAS on NVIDIA Blackwell to power ultra-low-latency inference and long context code generation for Cursor. From building megakernels that fuse an entire model's forward pass into a single launch to enabling real-time voice agents, Together AI is optimizing latency at every layer of the stack.
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AI that responds in under 100ms doesn't happen by accident. Together AI VP of Kernels Dan Fu shares how they use NVIDIA CUDA, TensorRT-LLM, Dynamo and Together ATLAS on NVIDIA Blackwell to power ultra-low-latency inference and long context code generation for Cursor. From building megakernels that fuse an entire model's forward pass into a single launch to enabling real-time voice agents, Together AI is optimizing latency at every layer of the stack.

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Day 14/90 of Inference Engineering I didn't finish the MNIST in CUDA project today, which made me hesitant to post since I didn't want to put out some slop. But in the spirit of being consistent and showing up, here's a concept I've revisited today now that I've started writing CUDA again. This is an animation on the CUDA memory life cycle. Generally speaking, when writing CUDA, memory management goes through the following stages: 1. Allocate memory on the host 2.Initialize / populate the memory 3.Allocate device memory 4.Copy host data to the device 5. Launch the kernel 6. Copy device data back to the host 7. Free the memory - On a brief reflection, I'm starting to write the matmul kernels in CUDA instead of in C. So this in itself is another paradigm shift in thinking. Excited to hone my technique over the coming week. I'll update you guys when I've completed the project; otherwise I'll just make a post like usual on what I've learned during the day!
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Day 14/90 of Inference Engineering I didn't finish the MNIST in CUDA project today, which made me hesitant to post since I didn't want to put out some slop. But in the spirit of being consistent and showing up, here's a concept I've revisited today now that I've started writing CUDA again. This is an animation on the CUDA memory life cycle. Generally speaking, when writing CUDA, memory management goes through the following stages: 1. Allocate memory on the host 2.Initialize / populate the memory 3.Allocate device memory 4.Copy host data to the device 5. Launch the kernel 6. Copy device data back to the host 7. Free the memory - On a brief reflection, I'm starting to write the matmul kernels in CUDA instead of in C. So this in itself is another paradigm shift in thinking. Excited to hone my technique over the coming week. I'll update you guys when I've completed the project; otherwise I'll just make a post like usual on what I've learned during the day!

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Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS
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Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS

TBPN

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We're excited to back Sail Research, the inference platform built for long-horizon, asynchronous agents. The next wave of AI isn't a person typing into a box. It's agents running for minutes or hours, burning orders of magnitude more tokens per task. At that scale, cost and capacity beat raw latency, and teams are already hitting budget caps. Neil Movva and Samir Menon are solving this full-stack, from CUDA kernels to the API. Hear Neil and Aditya Naganath on why inference is being rebuilt for the age of agents.

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2:24

Nvidia announces the new RTX Spark, a new platform powered by the NX1 CPU, and shows off Spark laptops running 007 First Light and Forza 6. The CPU has 20 ARM based cores and a Blackwell RTX GPU with 6144 CUDA Cores. This is the same core count as a 5070, but with 128GB of unified LPDDR5X RAM memory sitting in the same package as the CPU and GPU. The entire Nvidia software stack is available, particularly CUDA, vital for AI. Nvidia's new laptops will likely be ideal for running local LLMs be cause the unified memory means you can load models up to 120-180B parameters (quantized). These laptops are expected to ship later this year and could become strong competitors to high-end MacBooks and even Mac Studios for local AI workloads, thanks to CUDA support and unified memory. Price is unannounced.

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The CUDA moat is real, but probably not for long, says CEO of AI infrastructure platform Modal Erik Bernhardsson. He says he's bullish on alternative accelerators over the 2-3 year timeline, even though there's currently zero demand from his customers for TPUs, etc. "The cost today of rewriting your software to run on those stacks is very high... But the cost is going to go down." "You're going to have software that basically lets you take CUDA-compatible stuff and run it on alternative accelerators."

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Chamath: US AI Startups Can Learn A Lot from DeepSeek "This is a case where necessity was the mother of invention." On E213, Chamath Palihapitiya explained how, even if the $6M number is inaccurate, DeepSeek still had some impressive breakthroughs: GRPO > PPO for reinforcement learning "These guys were like, 'Well, how am I going to do this reinforcement learning thing?' They invented a totally different algorithm." "It uses a lot less computer memory and it's highly performant." PTX > CUDA to build the model "And then the second thing that was crazy is everybody is used to building models and compiling through CUDA, which is NVIDIA's proprietary language." "And these guys worked totally around CUDA and they did something called PTX, which goes right to the bare metal." What US AI startups can learn from this: "We, meaning the West, with all the money that we've had, didn't come up with these ideas." "And I think part of why we didn't come up is not that we're not smart enough to do it, but we weren't forced to because the constraints didn't exist." "And so I just wonder how we make sure we learn this principle, meaning, when the AI company wakes up and rolls out of bed and some VC gives them $200M, maybe that's not the right answer for a Series A or a Seed." "And maybe the right answer is $2M so that they do these DeepSeek-like innovations."

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Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.

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World Labs CEO Dr. Fei-Fei Li says AI must change how we teach and evaluate students: "AI must change learning. AI must change K-16 learning." "The most precious resource of our entire world is human capital." "When we have gotten the technology that can answer standardized tests... when AI can do better than an average human, it's not about humans are bad. It's about we need to change the education system." "We need to change how we evaluate. We need to change the way we empower teachers to educate the next generation of students where they can use these tools, be empowered, and do things that we can never imagine." "All of the kids today should not be scared of AI. They should feel the human agency to lead AI, to use AI in the right way, and to use AI to make the impact that they want to make for the world." Fei-Fei Li at Bloomberg Tech live with Emily Chang

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Modal engineer who teaches Full-Stack Deep Learning explained everything an AI engineer needs to know about GPUs in 20 minutes - better than $800 CUDA workshops. memory bandwidth -> SM occupancy -> kernel fusion -> arithmetic intensity -> know when you're compute-bound vs memory-bound. That loop is how you stop overpaying for GPUs you're barely using. VRAM math + roofline model + Modal serverless GPUs + kernel profiling - that's the stack. Watch and save it, then check your own GPU utilization tonight.

h100envy

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34:46

Ex-vLLM core contributor explained how to make LLM inference 10x cheaper in 34 minutes - better than $3000 inference optimization bootcamps. request comes in -> check LMCache -> hit? load KV cache from CPU/SSD/remote -> skip prefill -> serve. That loop is why Bloomberg and other production stacks now push 300 terabytes of KV cache per week. LMCache + vLLM + CPU/SSD/remote storage + zero-copy CUDA kernels - that's the stack. Watch and save it, then wire the KV-offload into your inference stack.

h100envy

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Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)
1:07

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

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2:14

Windsurf CEO explains why traditional startup moats are mostly silly. Varun said even with 50-100 of the best MIT engineers, that's still just "hundreds of engineering years" in your product. Someone else can build something similar in the same space. He used Nvidia as an example. People think CUDA is their real moat, but that's not accurate. Large companies like Google, OpenAI, and Anthropic spend tens of billions on chips. If CUDA didn't exist, they'd find a way to write assembly code and make GPUs work. People use CUDA because Nvidia made it really capable at low floating-point math, great interconnect, and fast computers. Not because they're locked in. Every year Nvidia has pressure - if they don't make hardware, interconnect, and memory bandwidth much faster, their profit margins shrink and AMD will compete. For startups, the only moat is speed. Learning where the dead bodies are. Understanding what doesn't work and building compounding advantages through that knowledge. Speed lets you learn from the market faster, which means you're first to the next idea too.

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We’re excited to announce that our AI agent, Tutorial Agent, is now integrated into the CZanga AI terminal to assist with web3 and blockchain learning CZanga is building a cutting-edge AI agents application and contributing to the AI layer of the iDos Games Engine This partnership opens the door for joint R&D as we explore the full potential of LLMs together.
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We’re excited to announce that our AI agent, Tutorial Agent, is now integrated into the CZanga AI terminal to assist with web3 and blockchain learning CZanga is building a cutting-edge AI agents application and contributing to the AI layer of the iDos Games Engine This partnership opens the door for joint R&D as we explore the full potential of LLMs together.

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“Today’s LLMs are the ‘Mainframe Computers’ of our generation” I was on Bloomberg TV today discussing Sakana AI, and to share my view that today’s LLMs are our generation’s “Mainframe Computers”. We are still in the very early stages of AI, and it is inevitable, due to market competition and global innovation (especially from those innovating with resource constraints), that this technology will become a million times more efficient. There is a narrative established in Silicon Valley that AI is a “Winner Takes All” technology, and that scaling up existing models and consuming ever greater resources will require (and even justify) the largest investments of our generation, in order to “win” the AI race. In contrast, I believe that AI is not a “winner takes all” technology. LLMs will be commoditized, become vastly more efficient, and made widely available in all countries. Ultimately, there will be thousands, if not millions, of AI models used by everyone. Just like with the evolution of early clunky mainframe computers to modern computing, how we use AI today will look very different in a few years (or even a year from now), compared to today’s ‘clunky’ LLMs.
7:17

“Today’s LLMs are the ‘Mainframe Computers’ of our generation” I was on Bloomberg TV today discussing Sakana AI, and to share my view that today’s LLMs are our generation’s “Mainframe Computers”. We are still in the very early stages of AI, and it is inevitable, due to market competition and global innovation (especially from those innovating with resource constraints), that this technology will become a million times more efficient. There is a narrative established in Silicon Valley that AI is a “Winner Takes All” technology, and that scaling up existing models and consuming ever greater resources will require (and even justify) the largest investments of our generation, in order to “win” the AI race. In contrast, I believe that AI is not a “winner takes all” technology. LLMs will be commoditized, become vastly more efficient, and made widely available in all countries. Ultimately, there will be thousands, if not millions, of AI models used by everyone. Just like with the evolution of early clunky mainframe computers to modern computing, how we use AI today will look very different in a few years (or even a year from now), compared to today’s ‘clunky’ LLMs.

hardmaru

165,382 görüntüleme • 1 yıl önce

We’re redefining what’s possible with AI. With the release of our latest model, Command A, optimized for real-world agentic and multilingual tasks, we’re demonstrating our commitment to bringing enterprises AI that goes beyond the ordinary, and offers security & efficiency. Our team has developed highly capable and efficient models that can be run on just 2 GPUs. Check out our tech report to learn more:
0:30

We’re redefining what’s possible with AI. With the release of our latest model, Command A, optimized for real-world agentic and multilingual tasks, we’re demonstrating our commitment to bringing enterprises AI that goes beyond the ordinary, and offers security & efficiency. Our team has developed highly capable and efficient models that can be run on just 2 GPUs. Check out our tech report to learn more:

Cohere

16,426 görüntüleme • 1 yıl önce