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gpu kernels on on my computer writing other gpu kernels for the same chip to simulate 100ks of particles, each an agent + neural network sensing K bg fields and allocating energy to A actions, at 1000s steps/s, for thermodynamic artificial life i think maybe not a bubble lads

Fraser Price

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32,595 просмотров • 15 дней назад •via X (Twitter)

Здоровье и велнес Образование Наука и технологии

Anya Rossi• Live Now

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Today I release new GPU Particle Volumes system and the GPU Snow Particle shaders for it! This snow shader can render hundreds of thousands of particles at the same time even on a weak GPU. It also supports VRC Light Volumes. Get it on my Booth or Patreon!

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It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

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Sharon Zhou

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Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

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I implemented Google Research's TurboQuant as a CUDA-native compression engine on Blackwell B200. 5x KV cache compression on Qwen 2.5-1.5B, near-loseless attention scores, generating live from compressed memory. 5 custom cuTile CUDA kernels ft: - fused attention (with QJL corrections) - online softmax -on-chip cache decompression - pipelined TMA loads Try it out: s/o Bryce, the CUDA Colonel and the cuTile team at NVIDIA for lending me Blackwell GPU access :) cc sunny madra Gavin

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every other day i yearn for more research fields to adopt the culture from the ML and ML adjacent fields of building a demo website for each paper and put code on github

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outside five sigma

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NO1ennn

25,119 просмотров • 1 месяц назад

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PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

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TBPN

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Mark Skol, Jr.

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DeepSeek-v4 now runs at ~23-26 tok/s on MLX! I made some custom kernels for the sinkhorn and it took gen speeds for 17 -> 26 tok/s. The weights are also significantly smaller thanks to Pedro Cuenca tip about keeping the experts in MXFP4! Now you can use it to power your local coding agents (PI, Open code, Hermes agent or even CC) PR:

DeepSeek-v4 now runs at ~23-26 tok/s on MLX! I made some custom kernels for the sinkhorn and it took gen speeds for 17 -> 26 tok/s. The weights are also significantly smaller thanks to Pedro Cuenca tip about keeping the experts in MXFP4! Now you can use it to power your local coding agents (PI, Open code, Hermes agent or even CC) PR:

Prince Canuma

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“I do not think it is a coincidence that the writing of a woman brought me here. I am so grateful to the women in my life. I cannot overstate the importance of intersectionality to me. We need to look for friends and allies here, and not enemies.”

“I do not think it is a coincidence that the writing of a woman brought me here. I am so grateful to the women in my life. I cannot overstate the importance of intersectionality to me. We need to look for friends and allies here, and not enemies.”

Jacob Tierney Updates

37,050 просмотров • 3 месяцев назад

I created a web app for teaching computer vision basics, allowing users to upload an image or video stream and instantly view it as a numerical pixel matrix, with options to display values in RGB, Hex, or Grayscale formats. It features predefined convolution kernels applied in real-time: - Sobel operator for edge detection (computes gradients to highlight boundaries) - Gaussian Blur: averages neighboring pixels with weights to reduce noise - Sharpen: boosts local contrast to enhance details This setup clearly visualizes how images are represented as matrices or tensors, how convolution slides kernels across pixels, and how operations like gradient computation, blurring, or sharpening modify the data step-by-step. I plan to use demos like this in my upcoming computer vision course to build intuition for fundamental image processing concepts, OpenCV filters, and CNN building blocks (e.g., why kernels extract features).

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