Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

LightVAE + ComfyUI node: High-performance video VAE; runs 2–3x faster using 50% less memory; LightTAE offers a 10+x speedup on just ~0.4GB VRAM

Wildminder

9,880 subscribers

38,092 просмотров • 9 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Microsoft just a 1-bit LLM with 2B parameters that can run on CPUs like Apple M2. BitNet b1.58 2B4T outperforms fp LLaMA 3.2 1B while using only 0.4GB memory versus 2GB and processes tokens 40% faster. 100% opensource.

Microsoft just a 1-bit LLM with 2B parameters that can run on CPUs like Apple M2. BitNet b1.58 2B4T outperforms fp LLaMA 3.2 1B while using only 0.4GB memory versus 2GB and processes tokens 40% faster. 100% opensource.

Shubham Saboo

260,049 просмотров • 1 год назад

EAGLE-3 introduces two key innovations: Training-Time Testing (TTT) and multi-level feature fusion. By removing the feature prediction constraint used in previous EAGLE versions and leveraging semantic features across multiple layers, EAGLE-3 achieves higher acceptance rates, faster generation, and lossless performance. The result? - 5.6× faster than vanilla decoding (13B) Compared to EAGLE-1, EAGLE-3 delivers a 1.8× speedup on the 13B model, with the future EAGLE-4 release expected to further improve decoding efficiency. *Inference on the video conducted on 2x RTX 3090 GPUs at fp16 precision using the Vicuna 13B model.

EAGLE-3 introduces two key innovations: Training-Time Testing (TTT) and multi-level feature fusion. By removing the feature prediction constraint used in previous EAGLE versions and leveraging semantic features across multiple layers, EAGLE-3 achieves higher acceptance rates, faster generation, and lossless performance. The result? - 5.6× faster than vanilla decoding (13B) Compared to EAGLE-1, EAGLE-3 delivers a 1.8× speedup on the 13B model, with the future EAGLE-4 release expected to further improve decoding efficiency. *Inference on the video conducted on 2x RTX 3090 GPUs at fp16 precision using the Vicuna 13B model.

Eagle

37,053 просмотров • 1 месяц назад

AI is revolutionising film industry Google Veo 2 is now on LTX studio, you can generate high quality video in seconds with 1/3 original price i just create a commercial for CHANEL perfume in 2 hours 10 raw clips:

AI is revolutionising film industry Google Veo 2 is now on LTX studio, you can generate high quality video in seconds with 1/3 original price i just create a commercial for CHANEL perfume in 2 hours 10 raw clips:

el.cine

67,290 просмотров • 1 год назад

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,387 просмотров • 1 месяц назад

Researchers made KMeans 200x faster. And the new technique also beats approaches like cuML and FAISS. Flash-KMeans is an IO-aware implementation of exact KMeans that redesigns the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 33x speedup over cuML - 200x speedup over FAISS This speedup comes from how it moves through GPU memory. Standard KMeans runs in two steps, and both are bottlenecked by reads and writes to GPU memory: 1) The first step matches every point to its nearest centroid. Standard KMeans computes the full point-to-centroid distance matrix, writes it out to GPU memory, then reads it back to find each nearest centroid. That write-then-read round trip is the bottleneck. Flash-KMeans combines the distance calculation with the nearest-centroid step, so the result is computed on-chip and the full matrix is never written out. 2) The second step recomputes each centroid by averaging the points assigned to it. Standard KMeans has thousands of threads writing into the same centroid slots at once, so they stall waiting for their turn. Flash-KMeans sorts points by cluster first, turning scattered writes into sequential reductions that read and write memory in one efficient pass. Using these two optimizations at the million-scale, Flash-KMeans completes a standard KMeans iteration in a few milliseconds. The video below depicts this in action. Several reasons why this is important: KMeans has always been an offline primitive. Something you run once to preprocess data and move on. These speedups make the approach viable in several runtime-critical systems. ↳ Vector indices like FAISS use KMeans to build search indices. Faster KMeans means you can re-index dynamically as data changes. ↳ LLM quantization methods need KMeans to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Flash-KMeans makes it viable to run this inside the inference loop, not just in preprocessing. I have shared the paper in the replies. That said, memory is the real constraint Flash-KMeans solves, and the problem is not just limited to clustering. The vectors a RAG system stores after indexing create similar bottlenecks. I wrote a detailed walkthrough recently on cutting this vector memory by 32x with binary quantization, querying 36M+ vectors in a few milliseconds. Read it below.

Researchers made KMeans 200x faster. And the new technique also beats approaches like cuML and FAISS. Flash-KMeans is an IO-aware implementation of exact KMeans that redesigns the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 33x speedup over cuML - 200x speedup over FAISS This speedup comes from how it moves through GPU memory. Standard KMeans runs in two steps, and both are bottlenecked by reads and writes to GPU memory: 1) The first step matches every point to its nearest centroid. Standard KMeans computes the full point-to-centroid distance matrix, writes it out to GPU memory, then reads it back to find each nearest centroid. That write-then-read round trip is the bottleneck. Flash-KMeans combines the distance calculation with the nearest-centroid step, so the result is computed on-chip and the full matrix is never written out. 2) The second step recomputes each centroid by averaging the points assigned to it. Standard KMeans has thousands of threads writing into the same centroid slots at once, so they stall waiting for their turn. Flash-KMeans sorts points by cluster first, turning scattered writes into sequential reductions that read and write memory in one efficient pass. Using these two optimizations at the million-scale, Flash-KMeans completes a standard KMeans iteration in a few milliseconds. The video below depicts this in action. Several reasons why this is important: KMeans has always been an offline primitive. Something you run once to preprocess data and move on. These speedups make the approach viable in several runtime-critical systems. ↳ Vector indices like FAISS use KMeans to build search indices. Faster KMeans means you can re-index dynamically as data changes. ↳ LLM quantization methods need KMeans to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Flash-KMeans makes it viable to run this inside the inference loop, not just in preprocessing. I have shared the paper in the replies. That said, memory is the real constraint Flash-KMeans solves, and the problem is not just limited to clustering. The vectors a RAG system stores after indexing create similar bottlenecks. I wrote a detailed walkthrough recently on cutting this vector memory by 32x with binary quantization, querying 36M+ vectors in a few milliseconds. Read it below.

Avi Chawla

89,234 просмотров • 1 месяц назад

NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

Lior Alexander

158,565 просмотров • 3 лет назад

90% of "AI developers" just download pre packaged GGUF files from Hugging Face, hit run, and call it a day. The top 10% know how to pull the raw safetensors, run the math, and quantize massive models into Q4_K_M themselves. If you think llama.cpp can only execute models, you’re missing the best part of the open source ecosystem. It’s a high performance optimization suite. Manually stripping 69% of the VRAM footprint off a brand new model architecture is where real infrastructure value is made. If you want to actually master local inference and deploy models like Google’s massive Gemma 4 12B it on consumer NVIDIA hardware using llama.cpp, you need to learn this pipeline. Let's build it. I just took the raw 22.7 GB Gemma 4 baseline and manually compressed it down to a 7.02 GB Q4_K_M GGUF artifact using llama.cpp. That is a 69% reduction in footprint. No quality loss. No VRAM bottlenecks. Just native, hardware accelerated C++ inference running a full 2,50,000 token context window on a dual NVIDIA Tesla T4 setup. Stop melting your VRAM on unoptimized weights and stop relying on other people's pipelines. Own your stack. I mapped this entire architecture from dynamic binary fetching to raw quantization and real time GPU streaming into a single, bulletproof notebook. Notebook link is in the comments below. Bookmark this blueprint for your next deployment and tell me which quantization works best for your workflow and model.

Alok

62,631 просмотров • 1 месяц назад

Into the multiverse, we go 🔮 AshPerp 🔥 is the first perpetual DEX built by AshSwap 🔥 on Multiversᕽ and is now powered by Pyth. Learn more about the integration below: ℹ️ About AshPerp AshPerp offers up to 100x leverage on crypto and other assets in a CEX-like, liquidity-efficient, and seamless futures trading experience. AshPerp uses USDC as collateral for all trades, regardless of the trading pair, with synthetic leverage and a dedicated single-staking USDC Vault. Features include: - 1-Click Trading Wallet: x2 faster processing speed, free EGLD gas fee for market orders. - Referral Scheme: 10% fee discount for referrals and tier-based fee rebates for referrers (up to 10%) - AshGuard Utility NFTs: save up to 50% on open fees and 50% on fixed spreads. 🔮 AshPerp is powered by Pyth AshPerp is using Pyth’s extensive oracle services and its Price Feeds to ensure access to its low-latency and high-quality financial data across multiple blockchains.

Into the multiverse, we go 🔮 AshPerp 🔥 is the first perpetual DEX built by AshSwap 🔥 on Multiversᕽ and is now powered by Pyth. Learn more about the integration below: ℹ️ About AshPerp AshPerp offers up to 100x leverage on crypto and other assets in a CEX-like, liquidity-efficient, and seamless futures trading experience. AshPerp uses USDC as collateral for all trades, regardless of the trading pair, with synthetic leverage and a dedicated single-staking USDC Vault. Features include: - 1-Click Trading Wallet: x2 faster processing speed, free EGLD gas fee for market orders. - Referral Scheme: 10% fee discount for referrals and tier-based fee rebates for referrers (up to 10%) - AshGuard Utility NFTs: save up to 50% on open fees and 50% on fixed spreads. 🔮 AshPerp is powered by Pyth AshPerp is using Pyth’s extensive oracle services and its Price Feeds to ensure access to its low-latency and high-quality financial data across multiple blockchains.

Pyth Network 🔮

83,590 просмотров • 2 лет назад

Nobody is talking about what this Nvidia laptop actually means for creators Jensen Huang walked on stage and held up the RTX Spark like it was nothing special. Full Blackwell GPU. 1 petaflop of AI performance inside a laptop. It renders, edits and runs local AI models faster than most desktop setups people have at home. You pay zero monthly fees because everything runs locally on the device. Creators who switch stop paying $300 a month in tools immediately. Video editors are already charging $150 an hour using the AI workflows this chip makes possible. At 8 hours a day that is $3,600 a week from a single laptop. Most people are still waiting 3 hours for a render this thing finishes in 10 minutes. The reaction in this clip says everything. Follow if you want to know what actually matters before everyone else figures it out.

Nobody is talking about what this Nvidia laptop actually means for creators Jensen Huang walked on stage and held up the RTX Spark like it was nothing special. Full Blackwell GPU. 1 petaflop of AI performance inside a laptop. It renders, edits and runs local AI models faster than most desktop setups people have at home. You pay zero monthly fees because everything runs locally on the device. Creators who switch stop paying $300 a month in tools immediately. Video editors are already charging $150 an hour using the AI workflows this chip makes possible. At 8 hours a day that is $3,600 a week from a single laptop. Most people are still waiting 3 hours for a render this thing finishes in 10 minutes. The reaction in this clip says everything. Follow if you want to know what actually matters before everyone else figures it out.

winkle.

14,477 просмотров • 2 месяцев назад

WHY THE $KOSPI KEEPS CRASHING. 🚨 Five things are driving the violence. 1. It runs on retail: quick flip mentality turns every dip into a crash and every bounce into a spike. 2. Samsung and SK Hynix alone are nearly half the entire index. Two stocks move the whole country. 3. Margin debt hit a record 38 trillion won. Leveraged ETFs on Samsung and SK Hynix double every move, forcing faster selling. 4. The won is a local currency, not a global reserve. Foreign selloffs hit harder with less support. 5. SK Hynix just passed Samsung as Korea's most valuable company. Elevated volatility is here to stay for all markets.

WHY THE $KOSPI KEEPS CRASHING. 🚨 Five things are driving the violence. 1. It runs on retail: quick flip mentality turns every dip into a crash and every bounce into a spike. 2. Samsung and SK Hynix alone are nearly half the entire index. Two stocks move the whole country. 3. Margin debt hit a record 38 trillion won. Leveraged ETFs on Samsung and SK Hynix double every move, forcing faster selling. 4. The won is a local currency, not a global reserve. Foreign selloffs hit harder with less support. 5. SK Hynix just passed Samsung as Korea's most valuable company. Elevated volatility is here to stay for all markets.

Crypto Rover

104,092 просмотров • 1 месяц назад

you're paying $20/mo for something your $500 GPU can already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.

you're paying $20/mo for something your $500 GPU can already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.

Alok

36,031 просмотров • 1 месяц назад

THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf

THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf

Fokki

12,933 просмотров • 1 месяц назад

Each week I do two back to back long runs. I’m in the THICK of training for my next 100miler. This week, the 2 back-to-back runs equaled nearly 12hrs of running, 5:45hrs Tuesday and 6hrs Wednesday. I was in Phoenix for Thanksgiving with family so I was able to run in the desert /warm conditions just east of the city - good training for my HURT 100miler in January (on Oahu). Also very technical! Back to back long runs allow me to do two hard endurance efforts on consecutive days without the same body fatigue/impact I’d get from just doing a single day 12hr run. First 2 pics are from Day 1 run, second 2 (pic/video) from day 2. Was definitely tired by the end of day 2. But felt good most the run. Need to dial in my calories better. Had to carry all my water for nearly 6hrs for day 1. Very heavy pack (13 pounds in a Salomon 12L). Day 2 had a water refill halfway through, still good amount of weight carried, but way less than day 1 (took 48oz + calories).

Each week I do two back to back long runs. I’m in the THICK of training for my next 100miler. This week, the 2 back-to-back runs equaled nearly 12hrs of running, 5:45hrs Tuesday and 6hrs Wednesday. I was in Phoenix for Thanksgiving with family so I was able to run in the desert /warm conditions just east of the city - good training for my HURT 100miler in January (on Oahu). Also very technical! Back to back long runs allow me to do two hard endurance efforts on consecutive days without the same body fatigue/impact I’d get from just doing a single day 12hr run. First 2 pics are from Day 1 run, second 2 (pic/video) from day 2. Was definitely tired by the end of day 2. But felt good most the run. Need to dial in my calories better. Had to carry all my water for nearly 6hrs for day 1. Very heavy pack (13 pounds in a Salomon 12L). Day 2 had a water refill halfway through, still good amount of weight carried, but way less than day 1 (took 48oz + calories).

Candice

20,556 просмотров • 1 год назад

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!

Daily Dose of Data Science

23,748 просмотров • 3 месяцев назад

🎃 Halloween Contest: Handmade Magic Awaits! 🦇 Jelly’s spooky adventure is here and it’s all about YOU crafting the magic! Carve a pumpkin, bake something creepy, design a costume, snap a spooky photo... as long as it’s handmade & has a Portal to Bitcoin twist. ✨ 🕯 How to Enter: 1⃣ Create something spooky with a nod to Portal 2⃣ Post it on X with #HalloweenMagic + tag us 3⃣ On Discord drop the link in our halloween channel 👻 Prizes for Top 10: 🥇 $50 | 🥈 $40 | 🥉 $25 | 4th $20 | 5th $15 | 6–10th $10 📅 Contest runs Oct 22 – Nov 2 (11:59 pm UTC) Enter… if you dare. 👻🎃

🎃 Halloween Contest: Handmade Magic Awaits! 🦇 Jelly’s spooky adventure is here and it’s all about YOU crafting the magic! Carve a pumpkin, bake something creepy, design a costume, snap a spooky photo... as long as it’s handmade & has a Portal to Bitcoin twist. ✨ 🕯 How to Enter: 1⃣ Create something spooky with a nod to Portal 2⃣ Post it on X with #HalloweenMagic + tag us 3⃣ On Discord drop the link in our halloween channel 👻 Prizes for Top 10: 🥇 $50 | 🥈 $40 | 🥉 $25 | 4th $20 | 5th $15 | 6–10th $10 📅 Contest runs Oct 22 – Nov 2 (11:59 pm UTC) Enter… if you dare. 👻🎃

Portal

12,129 просмотров • 9 месяцев назад

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!

Akshay 🚀

36,317 просмотров • 4 месяцев назад

AI will kill Polymarket. $2.2M in 2 months using probability models. This news is going to blow up the internet. Polymarket trader made $2.2M in just 2 months using AI. His account is traded entirely by a bot. I’ve heard plenty of stories about AI trading bots before, and almost all of them turned out to be scams or didn’t work properly. But this case is different and honestly I’m shocked. He uses AI probability models, training machine learning to estimate real odds based on news and social media data. If his model says an outcome has a 60% chance, while the market prices it at 50% (50¢), he buys because the market is mispricing it. According to his profile: > His prediction accuracy is 74%. That’s insane. He runs an ensemble of 10 AI models that retrain themselves every week to stay up to date. What do you think about this? This feels like the new reality.

igorizuchaetcrypty

509,887 просмотров • 7 месяцев назад

the opportunity's AI UGC opened up for affiliate is absolutely insane I built an AI UGC system to promote sweeps offers on Glitchy just using Arcads + Claude Cowork Claude literally: > Scans TikTok / Reddit based on what I'm promoting > Generates me 50 hooks using this information > Creates me a full script based on the hooks > Uses the Arcads API to decide what tools to use for the script and generates the video > Then will analyse what hooks did well and which didn't and change the hook or CTA based on that information I can literally sleep while it cooks me up a weeks worth of content I made a full guide on how to set Claude Cowork up to generate videos with Arcads if you want it comment "UGC" and i'll send it to you (Must be following so i can DM you)

the opportunity's AI UGC opened up for affiliate is absolutely insane I built an AI UGC system to promote sweeps offers on Glitchy just using Arcads + Claude Cowork Claude literally: > Scans TikTok / Reddit based on what I'm promoting > Generates me 50 hooks using this information > Creates me a full script based on the hooks > Uses the Arcads API to decide what tools to use for the script and generates the video > Then will analyse what hooks did well and which didn't and change the hook or CTA based on that information I can literally sleep while it cooks me up a weeks worth of content I made a full guide on how to set Claude Cowork up to generate videos with Arcads if you want it comment "UGC" and i'll send it to you (Must be following so i can DM you)

Pounds

17,180 просмотров • 4 месяцев назад

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

underwood

32,399 просмотров • 2 месяцев назад

Kosher media caught lying again! The U.S. army fell for it again! 1. Iran is using anamorphic ground paintings to deceive US and Israeli attacks. Look closely at this video on the left released by the IDF — the so-called ‘destroyed Mi-17 helicopter’ isn’t a helicopter at all… it’s just a painting on the asphalt! There’s that high kosher IQ! 2. Trump says Iran's Navy has been sent to the bottom of the sea, totally destroyed. Meanwhile, the Iranian Navy successfully targeted a US Navy destroyer ship.

Kosher media caught lying again! The U.S. army fell for it again! 1. Iran is using anamorphic ground paintings to deceive US and Israeli attacks. Look closely at this video on the left released by the IDF — the so-called ‘destroyed Mi-17 helicopter’ isn’t a helicopter at all… it’s just a painting on the asphalt! There’s that high kosher IQ! 2. Trump says Iran's Navy has been sent to the bottom of the sea, totally destroyed. Meanwhile, the Iranian Navy successfully targeted a US Navy destroyer ship.

Truth_teller 🇷🇺

59,245 просмотров • 5 месяцев назад