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Long video generation is a systems problem. Introducing LongLive-2.0 from NVIDIA Research: an end-to-end NVFP4 training and inference system for long video generation. Low-precision deployment often relies on post-training quantization, creating a gap between how models are trained and how they run. LongLive-2.0 aligns NVFP4-aware training, distillation, and W4A4... show more

NVIDIA AI

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60,112 görüntüleme • 16 gün önce •via X (Twitter)

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🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

Yukang Chen

56,735 görüntüleme • 19 gün önce

I spent my summer building TinyTPU : An open source ML inference and training chip. it can do end to end inference + training ENTIRELY on chip. here's how I did it👇:

I spent my summer building TinyTPU : An open source ML inference and training chip. it can do end to end inference + training ENTIRELY on chip. here's how I did it👇:

surya

354,649 görüntüleme • 9 ay önce

🚀 We open-sourced LongLive — interactive, real-time long-video generation. 👥Generates video in real time as users enter text prompts. ⚡️20.7 FPS on a single H100,⏱️up to 240s per clip. 🎬Fine-tunes SOTA short-video models (e.g., Wan) into long-video generators. 🌍One step closer to World Models. All code for training & inference, model weights, demo page, and videos released! Paper: Code: Model: Demo Page: Introduction Video:

🚀 We open-sourced LongLive — interactive, real-time long-video generation. 👥Generates video in real time as users enter text prompts. ⚡️20.7 FPS on a single H100,⏱️up to 240s per clip. 🎬Fine-tunes SOTA short-video models (e.g., Wan) into long-video generators. 🌍One step closer to World Models. All code for training & inference, model weights, demo page, and videos released! Paper: Code: Model: Demo Page: Introduction Video:

Yukang Chen

11,752 görüntüleme • 8 ay önce

Gemma and the power of post-training. 💪 Google DeepMind’s Léonard Hussenot details: 🔍 How Gemma facilitates post-training research 🚀 Best-of-N Distillation (BOND) for model efficiency 🏆 Improving reward models with weight averaging 👀 →

Gemma and the power of post-training. 💪 Google DeepMind’s Léonard Hussenot details: 🔍 How Gemma facilitates post-training research 🚀 Best-of-N Distillation (BOND) for model efficiency 🏆 Improving reward models with weight averaging 👀 →

Google for Developers

18,616 görüntüleme • 1 yıl önce

Meta announces Movie Gen A Cast of Media Foundation Models We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user’s image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models

Meta announces Movie Gen A Cast of Media Foundation Models We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user’s image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models

AK

62,702 görüntüleme • 1 yıl önce

This is why Nvidia bought Groq. If Groq deploys a ton of low-cost inference chips, demand for training will increase massively. The more inference you have, the more training you need, and vice versa. Nvidia can keep its high margins selling chips for training forever.

This is why Nvidia bought Groq. If Groq deploys a ton of low-cost inference chips, demand for training will increase massively. The more inference you have, the more training you need, and vice versa. Nvidia can keep its high margins selling chips for training forever.

Lin

92,571 görüntüleme • 5 ay önce

We are innovating the full-stack AI infrastructure platform on a one-year rhythm — pushing the boundaries of inference performance across chips, systems, and software. NVIDIA Blackwell is the most advanced AI platform ever built — continuously optimized for record-breaking performance. Groundbreaking advancements include the NVIDIA-designed NVFP4 format, enabling high inference performance and accuracy. #GTCParis at #VivaTech

We are innovating the full-stack AI infrastructure platform on a one-year rhythm — pushing the boundaries of inference performance across chips, systems, and software. NVIDIA Blackwell is the most advanced AI platform ever built — continuously optimized for record-breaking performance. Groundbreaking advancements include the NVIDIA-designed NVFP4 format, enabling high inference performance and accuracy. #GTCParis at #VivaTech

NVIDIA

80,342 görüntüleme • 1 yıl önce

Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote at #CES2026. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote at #CES2026. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

NVIDIA Data Center

13,159 görüntüleme • 4 ay önce

NVIDIA Cosmos 3 launches with a full model family — Cosmos Super for highest-accuracy robotics and AV post-training, Cosmos Nano for high-speed video and action reasoning, and Cosmos Edge for real-time edge inference. Read the release ➡️

NVIDIA Cosmos 3 launches with a full model family — Cosmos Super for highest-accuracy robotics and AV post-training, Cosmos Nano for high-speed video and action reasoning, and Cosmos Edge for real-time edge inference. Read the release ➡️

NVIDIA Newsroom

46,059 görüntüleme • 6 gün önce

World models are moving beyond offline generation towards interactive, real-time experiences. Introducing ⚡FlashDreams⚡: an open-source high-performance inference and serving library built for autoregressive world models: 🔥 Up to 3.10× faster LingBot-World inference 🔥 Up to 2.12× faster Self-Forcing inference 🔥 Up to 1.40× faster Wan2.1 inference 🔥 8 integrated models 🔥 Multi-GPU, streaming, low-latency serving 🔥 Agentic skills that teach you how to use it FlashDreams is designed for a new generation of AI systems that continuously evolve over time while responding to user interactions. It powers applications across robotics, autonomous vehicle simulation, gaming, and virtual worlds. Github: Docs: Research page: Join the #flashdreams Discord channel at FlashDreams is also the runtime backbone behind NVIDIA OmniDreams ( 1/n #AI #WorldModels #FastInference #PhysicalAI #OpenSource #NVIDIA

World models are moving beyond offline generation towards interactive, real-time experiences. Introducing ⚡FlashDreams⚡: an open-source high-performance inference and serving library built for autoregressive world models: 🔥 Up to 3.10× faster LingBot-World inference 🔥 Up to 2.12× faster Self-Forcing inference 🔥 Up to 1.40× faster Wan2.1 inference 🔥 8 integrated models 🔥 Multi-GPU, streaming, low-latency serving 🔥 Agentic skills that teach you how to use it FlashDreams is designed for a new generation of AI systems that continuously evolve over time while responding to user interactions. It powers applications across robotics, autonomous vehicle simulation, gaming, and virtual worlds. Github: Docs: Research page: Join the #flashdreams Discord channel at FlashDreams is also the runtime backbone behind NVIDIA OmniDreams ( 1/n #AI #WorldModels #FastInference #PhysicalAI #OpenSource #NVIDIA

Ruilong Li

64,390 görüntüleme • 4 gün önce

We open-sourced QeRL — Quantization-enhanced Reinforcement Learning ! 🧠 4-bit quantized RL training 💪 Train a 32B LLM on a single H100 GPU ⚙️ 1.7× faster overall training 🎯 Accuracy on par with bfloat16-level accuracy 🔥 Supports NVFP4 quantization format Moreover, we show that quantization helps exploration in RL training. Paper: Code: #NVIDIA #AIResearch #ReinforcementLearning #Quantization #LLM #EfficientAI

We open-sourced QeRL — Quantization-enhanced Reinforcement Learning ! 🧠 4-bit quantized RL training 💪 Train a 32B LLM on a single H100 GPU ⚙️ 1.7× faster overall training 🎯 Accuracy on par with bfloat16-level accuracy 🔥 Supports NVFP4 quantization format Moreover, we show that quantization helps exploration in RL training. Paper: Code: #NVIDIA #AIResearch #ReinforcementLearning #Quantization #LLM #EfficientAI

Yukang Chen

69,720 görüntüleme • 7 ay önce

Geometric Context Transformer for Streaming 3D Reconstruction Contributions: • We introduce LingBot-Map, a streaming 3D foundation model built around Geometric Context Attention (GCA), which maintains three complementary context types – anchor, pose-reference window, and trajectory memory – for efficient and consistent long-sequence streaming inference. • We propose an efficient training recipe based on progressive training and context parallelism with a relative loss formulation for stable long-sequence optimization. • We demonstrate that LingBot-Map achieves state-of-the-art performance on multiple benchmarks (Oxford Spires, Tanks and Temples, ETH3D, and 7-Scenes), significantly outperforming existing streaming approaches in reconstruction quality and inference speed.

Geometric Context Transformer for Streaming 3D Reconstruction Contributions: • We introduce LingBot-Map, a streaming 3D foundation model built around Geometric Context Attention (GCA), which maintains three complementary context types – anchor, pose-reference window, and trajectory memory – for efficient and consistent long-sequence streaming inference. • We propose an efficient training recipe based on progressive training and context parallelism with a relative loss formulation for stable long-sequence optimization. • We demonstrate that LingBot-Map achieves state-of-the-art performance on multiple benchmarks (Oxford Spires, Tanks and Temples, ETH3D, and 7-Scenes), significantly outperforming existing streaming approaches in reconstruction quality and inference speed.

MrNeRF

24,456 görüntüleme • 1 ay önce

Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this and made a highly strategic move by striking a deal with Groq. $NVDA is already dominating training, and now it's also well-positioned to dominate inference.

Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this and made a highly strategic move by striking a deal with Groq. $NVDA is already dominating training, and now it's also well-positioned to dominate inference.

Oguz Erkan

97,186 görüntüleme • 5 ay önce

Compute is the backbone of the AI-driven future. OptimAI Compute Engine would enable scalable, high-performance workloads across image and video models, supporting: • Text-to-image generation • Image editing and inpainting • 2K / 4K / 8K super-resolution • Brand-aligned visual synthesis • OCR and image intelligence • Video generation and motion synthesis • Frame interpolation and enhancement • Multimodal model inference Distributed compute built for faster inference, shorter training cycles, and production-grade AI execution.

OptimAI Network

18,733 görüntüleme • 3 ay önce

We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute budget, and Paris 2.0 outperformed the monolithic by ~2x on FVD benchmark.

We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute budget, and Paris 2.0 outperformed the monolithic by ~2x on FVD benchmark.

bidhan ✈️ CVPR

444,641 görüntüleme • 10 gün önce

Tired of fragmented AI solutions? 🤔 The Dell AI Factory with NVIDIA offers a complete end-to-end solution: advising, infrastructure, model training, and deployment, all on your own infrastructure. Learn how to gain insights and stay in control! @DellTechnologies @NVIDIA #Ad

Tired of fragmented AI solutions? 🤔 The Dell AI Factory with NVIDIA offers a complete end-to-end solution: advising, infrastructure, model training, and deployment, all on your own infrastructure. Learn how to gain insights and stay in control! @DellTechnologies @NVIDIA #Ad

Dell Technologies UK

4,288,759 görüntüleme • 1 yıl önce

At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.

At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.

The Render Network

10,904 görüntüleme • 1 yıl önce

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

119,268 görüntüleme • 2 ay önce

Introducing LongLive 2.0 by @NVIDIA. Real-time text-to-video with full multi-shot control. Build your story shot by shot, scene by scene. Available now on Reactor.

Introducing LongLive 2.0 by @NVIDIA. Real-time text-to-video with full multi-shot control. Build your story shot by shot, scene by scene. Available now on Reactor.

reactor

185,877 görüntüleme • 4 gün önce

.Reiner Pope's new blackboard lecture goes all the way down: how AI training and inference are built up from logic gates on silicon. He walks me through a 4-bit multiply-accumulate by hand, and shows how that primitive is the foundation for the matrix multiplies in training runs.

.Reiner Pope's new blackboard lecture goes all the way down: how AI training and inference are built up from logic gates on silicon. He walks me through a 4-bit multiply-accumulate by hand, and shows how that primitive is the foundation for the matrix multiplies in training runs.

Dwarkesh Patel

38,730 görüntüleme • 16 gün önce