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We're presenting at #SIGGRAPH2025! 🚀 We use adaptive tetrahedral grids (via the LEB algorithm) to GPU path trace volumetric production assets. Fewer neighbors, less memory, real-time performance—up to 30× faster than regular grids.

Anis Benyoub

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11,073 views • 1 year ago •via X (Twitter)

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OpenAI just announced Ultrafast a model that can run up to 300tok/s ! 8x faster . I’ve been testing the new Ultrafast for some time ! I had two agents playing Tekken on PS1 just by looking at images. They made one decision every 0.95 seconds. That enables for the first time near-real-time, high-intelligence interactions. This unlocks a bunch of new use cases!! 🚀

OpenAI just announced Ultrafast a model that can run up to 300tok/s ! 8x faster . I’ve been testing the new Ultrafast for some time ! I had two agents playing Tekken on PS1 just by looking at images. They made one decision every 0.95 seconds. That enables for the first time near-real-time, high-intelligence interactions. This unlocks a bunch of new use cases!! 🚀

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10,655 views • 10 days ago

Dylan Patel on the importance of memory and storage Two key quotes: "An $NVDA GPU is faster than an $AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads." “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly" Full Quote: “We have over $80 million of compute: GPUs from $NVDA and $AMD, TPUs from Google, and Trainium from Amazon. We constantly run this benchmark using the newest inference engines, drivers, PyTorch versions, and other software. It runs every day through automated CI across the latest Chinese models from GLM, Zhipu, Moonshot, Kimi, Alibaba, and others. Initially, when we were benchmarking the differences between these chips, inference engines, and parallelism schemes, we used fixed context lengths. But with Agent X, we have now analyzed more than $5 million worth of Claude Code traces. This is real production traffic that users have donated to us, combined with internally generated data, so we now understand what an actual agent workload looks like. When we implement those workloads and run the benchmarks, it turns out that the chip you are using is very important, but how you handle memory offload can be even more important. An Nvidia GPU is faster than an AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads. Similarly, you can use a less powerful GPU with a much better storage solution and outperform the best GPU when it lacks those solutions. Simply buying the newest GPU does not necessarily give you the best inference economics. You need to layer in other innovations, including storage and memory.” Interviewer: “Who is the top player on your chart? Can you tell us?” Dylan Patel: “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly.”

Dylan Patel on the importance of memory and storage Two key quotes: "An $NVDA GPU is faster than an $AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads." “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly" Full Quote: “We have over $80 million of compute: GPUs from $NVDA and $AMD, TPUs from Google, and Trainium from Amazon. We constantly run this benchmark using the newest inference engines, drivers, PyTorch versions, and other software. It runs every day through automated CI across the latest Chinese models from GLM, Zhipu, Moonshot, Kimi, Alibaba, and others. Initially, when we were benchmarking the differences between these chips, inference engines, and parallelism schemes, we used fixed context lengths. But with Agent X, we have now analyzed more than $5 million worth of Claude Code traces. This is real production traffic that users have donated to us, combined with internally generated data, so we now understand what an actual agent workload looks like. When we implement those workloads and run the benchmarks, it turns out that the chip you are using is very important, but how you handle memory offload can be even more important. An Nvidia GPU is faster than an AMD GPU in most cases, but because AMD GPUs have more memory, they can outperform Nvidia in certain workloads. Similarly, you can use a less powerful GPU with a much better storage solution and outperform the best GPU when it lacks those solutions. Simply buying the newest GPU does not necessarily give you the best inference economics. You need to layer in other innovations, including storage and memory.” Interviewer: “Who is the top player on your chart? Can you tell us?” Dylan Patel: “It is a difficult, multivariable problem. Generally, you need the best GPU, such as a GB300, but you also need the best storage solutions. I will not spoil who comes out on top, but storage solutions matter a lot, memory solutions matter a lot, and frontend networking also matters significantly.”

Daniel Romero

48,894 views • 3 months ago

Buddha says Collector Crypt has $30M in tokenized assets and its $CARDS token is backed by real revenue "The cards token is going to represent the entire amount of tokenized assets that we have on chain. It's going to represent the success of the business. I think we've got $30 million dollars in tokenized assets and counting" "Our CEO has talked about buybacks a number of times. I think they're certainly necessary and they are going to be. They're planned and they are going to be executed well in advance of anyone announcing that we're doing buybacks. Of course we're not here to give anyone a leg up on the token or anything. We prefer to acquire it at a bit of a lower price."

Buddha says Collector Crypt has $30M in tokenized assets and its $CARDS token is backed by real revenue "The cards token is going to represent the entire amount of tokenized assets that we have on chain. It's going to represent the success of the business. I think we've got $30 million dollars in tokenized assets and counting" "Our CEO has talked about buybacks a number of times. I think they're certainly necessary and they are going to be. They're planned and they are going to be executed well in advance of anyone announcing that we're doing buybacks. Of course we're not here to give anyone a leg up on the token or anything. We prefer to acquire it at a bit of a lower price."

Tig

14,949 views • 4 months ago

Today we're launching > the world's fastest TTS endpoint at 50 ms time-to-first-audio (TTFA) > the world's cheapest modern TTS endpoint at $5 / 1M characters Powered by Qwen3-TTS 1.7B on Nari Labs inference engine. 🚀 5x faster than Cartesia 💸 10x cheaper than ElevenLabs 🎙️ expressive voices, outperforming industry average 🎁 available FREE for a limited time We believe open-source will win: not just in LLMs, but also in multimodal AI. Nari Labs is here to accelerate that future, starting with speech. Try now at

Today we're launching > the world's fastest TTS endpoint at 50 ms time-to-first-audio (TTFA) > the world's cheapest modern TTS endpoint at $5 / 1M characters Powered by Qwen3-TTS 1.7B on Nari Labs inference engine. 🚀 5x faster than Cartesia 💸 10x cheaper than ElevenLabs 🎙️ expressive voices, outperforming industry average 🎁 available FREE for a limited time We believe open-source will win: not just in LLMs, but also in multimodal AI. Nari Labs is here to accelerate that future, starting with speech. Try now at

Toby Kim

43,680 views • 29 days ago

GDN: The $KNDX Solution No more hardware bottlenecks Kondux GDN offers streamlined solutions to the problems users may face these days, such as limited access to state-of-the-art hardware & GPU power. We solve this by offloading heavy computational tasks to GDN while streaming 3D assets in real time to any device, regardless of local device capabilities. This means users can easily perform complex activities like high performance gaming, 3D VFX rendering & digital twin simulations for manufacturing without reliance on local GPU power & hardware. For creators, developers & enterprises, this eliminates massive roadblocks for their creative work flow. Instant collaboration through blockchain security In addition, our smart contracts enables sharing digital assets instant and secure, allowing for easy collaboration across multiple platforms. By utilizing our DNA technology, viewing a 3D asset doesn’t automatically grant ownership, and ensures creators 3D assets are protected from unauthorized use. Our kNFT tech brings life, personality & evolution to digital assets. Part of our mission is facilitating the shift of conversation from only traditional static NFTs, towards dynamic high-fidelity 3D assets with real world utility. Advanced interactivity & immersion in real time for all.

GDN: The $KNDX Solution No more hardware bottlenecks Kondux GDN offers streamlined solutions to the problems users may face these days, such as limited access to state-of-the-art hardware & GPU power. We solve this by offloading heavy computational tasks to GDN while streaming 3D assets in real time to any device, regardless of local device capabilities. This means users can easily perform complex activities like high performance gaming, 3D VFX rendering & digital twin simulations for manufacturing without reliance on local GPU power & hardware. For creators, developers & enterprises, this eliminates massive roadblocks for their creative work flow. Instant collaboration through blockchain security In addition, our smart contracts enables sharing digital assets instant and secure, allowing for easy collaboration across multiple platforms. By utilizing our DNA technology, viewing a 3D asset doesn’t automatically grant ownership, and ensures creators 3D assets are protected from unauthorized use. Our kNFT tech brings life, personality & evolution to digital assets. Part of our mission is facilitating the shift of conversation from only traditional static NFTs, towards dynamic high-fidelity 3D assets with real world utility. Advanced interactivity & immersion in real time for all.

Kondux

50,669 views • 1 year ago