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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

23,748 просмотров

NN-SVG: Create neural network architecture drawings parametrically! Export them to SVG files and use them in your work!

NN-SVG: Create neural network architecture drawings parametrically! Export them to SVG files and use them in your work!

109,688 просмотров

NN-SVG: Create neural network architecture drawings parametrically! Export them to SVG files and use them in your work!

NN-SVG: Create neural network architecture drawings parametrically! Export them to SVG files and use them in your work!

70,805 просмотров

Visualizing complex data in Python just got easier! Meet Cosmograph for Python 🪐: The widget brings GPU-accelerated, interactive layout graph rendering right inside your Jupyter notebooks. Here’s why it’s a game-changer: ⚡ GPU-accelerated performance ⛓️ Interactive network exploration with pan, zoom, hover & selection ⚙️ Rich configuration APIs for layout, color, size & more 📦 Seamless notebook integration & easy Python installation But that’s not all: ✅ Force-directed simulations for dynamic layouts ✅ Smooth handling of large-scale networks ✅ Minimal setup—just pip install cosmograph Link to the repo in next tweet! ______ Follow us → Daily Dose of Data Science ✔️ For more insights & tutorials on AI and Machine Learning.

Visualizing complex data in Python just got easier! Meet Cosmograph for Python 🪐: The widget brings GPU-accelerated, interactive layout graph rendering right inside your Jupyter notebooks. Here’s why it’s a game-changer: ⚡ GPU-accelerated performance ⛓️ Interactive network exploration with pan, zoom, hover & selection ⚙️ Rich configuration APIs for layout, color, size & more 📦 Seamless notebook integration & easy Python installation But that’s not all: ✅ Force-directed simulations for dynamic layouts ✅ Smooth handling of large-scale networks ✅ Minimal setup—just pip install cosmograph Link to the repo in next tweet! ______ Follow us → Daily Dose of Data Science ✔️ For more insights & tutorials on AI and Machine Learning.

27,283 просмотров

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Claude Code used 3x fewer tokens with one change. - Before: 10.4M tokens · 10 errors · $9.21 - After: 3.7M tokens · 0 errors · $2.81 (read the full setup guide below)

Claude Code used 3x fewer tokens with one change. - Before: 10.4M tokens · 10 errors · $9.21 - After: 3.7M tokens · 0 errors · $2.81 (read the full setup guide below)

Daily Dose of Data Science

368,059 просмотров • 1 месяц назад

LLM inference speed with vs. without KV caching: (learn how and why it works below)

LLM inference speed with vs. without KV caching: (learn how and why it works below)

Daily Dose of Data Science

59,218 просмотров • 1 месяц назад

K-Means clustering, visually explained:

K-Means clustering, visually explained:

Daily Dose of Data Science

156,089 просмотров • 1 год назад

This is the best way to understand how ML models actually work! Use Drawdata to draw a 2D dataset in Jupyter. Use it to actively pick data from the widget and update the model as the data is being drawn! Fully interactive, real-time, and open-source!

This is the best way to understand how ML models actually work! Use Drawdata to draw a 2D dataset in Jupyter. Use it to actively pick data from the widget and update the model as the data is being drawn! Fully interactive, real-time, and open-source!

Daily Dose of Data Science

51,936 просмотров • 7 месяцев назад

Build RAG over excel sheets, a step-by-step guide:

Build RAG over excel sheets, a step-by-step guide:

Daily Dose of Data Science

15,737 просмотров • 1 год назад

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