Organizations are increasingly adopting hybrid and multi-cloud strategies to access the latest compute resources, consistently support worldwide customers, and optimize cost. However, a major challenge that engineering teams face is operationalizing AI applications across different platforms as the stack changes. This requires MLOps teams to familiarize themselves with different environments and developers to customize applications to run across target platforms.
NVIDIA offers a consistent, full stack to develop on a GPU-powered on-premises or on-cloud instance. You can then deploy that AI application on any GPU-powered platform without code changes.
The NVIDIA Cloud Native Stack Virtual Machine Image (VMI) is GPU-accelerated. It comes pre-installed with Cloud Native Stack, which is a reference architecture that includes upstream Kubernetes and the NVIDIA GPU Operator. NVIDIA Cloud Native Stack VMI enables you to build, test, and run GPU-accelerated containerized applications orchestrated by Kubernetes. — Read More
Tag Archives: Nvidia
Inside China’s underground market for high-end Nvidia AI chips
Psst! Where can a Chinese buyer purchase top-end Nvidia (NVDA.O) AI chips in the wake of U.S. sanctions?
Visiting the famed Huaqiangbei electronics area in the southern Chinese city of Shenzhen is a good bet – in particular, the SEG Plaza skyscraper whose first 10 floors are crammed with shops selling everything from camera parts to drones. The chips are not advertised but asking discreetly works. — Read More
Digital Renaissance: NVIDIA Neuralangelo Research Reconstructs 3D Scenes
Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures — generating lifelike virtual replicas of buildings, sculptures and other real-world objects.
Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures. Creative professionals can then import these 3D objects into design applications, editing them further for use in art, video game development, robotics and industrial digital twins. — Read More
What runs ChatGPT? Inside Microsoft’s AI supercomputer
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution 512 x 1024, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pre-trained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048. We show that the temporal layers trained in this way generalize to different fine-tuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Read More
Paper
AI Developers Stymied by Server Shortage at AWS, Microsoft, Google
Startups and other companies trying to capitalize on the artificial intelligence boom sparked by OpenAI are running into a problem: They can’t find enough specialized computers to make their own AI software.
A spike in demand for server chips that can train and run machine-learning software has caused a shortage, prompting major cloud-server providers including Amazon Web Services, Microsoft, Google and Oracle to limit their availability for customers, according to interviews with the cloud companies and their customers. Some customers have reported monthslong wait times to rent the hardware. Read More
Nvidia predicts AI models one million times more powerful than ChatGPT within 10 years
A million here, times a million there. Pretty soon you’re talking about big numbers. So Nvidia claims for its AI accelerating hardware in terms of the performance boost it has delivered over the last decade and will deliver again over the next 10 years.
The result, if Nvidia is correct, will be a new industry of AI factories across the world and gigantic breakthroughs in AI processing power. It also means, ostensibly, AI models one million times more powerful than existing examples, including ChatGPT, in AI processing terms at least. Read More
IBM says it’s been running ‘AI supercomputer’ since May but chose now to tell the world
Cloud-native Vela specializes in developing and training large-scale AI models – in-house only, though
IBM is the latest tech giant to unveil its own “AI supercomputer,” this one composed of a bunch of virtual machines running within IBM Cloud.
The system known as Vela, which the company claims has been online since May last year, is touted as IBM’s first AI-optimized, cloud-native supercomputer, created with the aim of developing and training large-scale AI models.
Before anyone rushes off to sign up for access, IBM stated that the platform is currently reserved for use by the IBM Research community. In fact, Vela has become the company’s “go-to environment” for researchers creating advanced AI capabilities since May 2022, including work on foundation models, it said. Read More
NVIDIA Omniverse Shines in a New Light with Magic3D
NVIDIA’s new text-to-3D synthesis model Magic3D creates high-quality 3D mesh models better than Google’s DreamFusion
Earlier this month, NVIDIA announced that it would be enabling the beta release of Omniverse, a platform where developers and creators can build Metaverse applications. In this way, the company has aligned its future along the metaverse vision, with the new platform allowing its users to create “digital twins” to simulate the real world.
One such step towards the realisation of such a dream that would help users to render a high-resolution 3D model for any 2D image input, or textual prompt, is Magic3D. Recently released by NVIDIA researchers, Magic3D is a text-to-3D synthesis model that creates high-quality 3D mesh models.
The model is a response to Google’s DreamFusion, in which the team used a pre-trained text-to-image diffusion model, circumventing the impossibility of having large-scale labelled 3D datasets, to optimise Neural Radiance Fields (NeRF). Read More
NVIDIA, Oracle CEOs in Fireside Chat Light Pathways to Enterprise AI
Speeding adoption of enterprise AI and accelerated computing, Oracle CEO Safra Catz and NVIDIA founder and CEO Jensen Huang discussed their companies’ expanding collaboration in a fireside chat live streamed today from Oracle CloudWorld in Las Vegas.
Oracle and NVIDIA announced plans to bring NVIDIA’s full accelerated computing stack to Oracle Cloud Infrastructure (OCI). It includes NVIDIA AI Enterprise, NVIDIA RAPIDS for Apache Spark and NVIDIA Clara for healthcare.
In addition, OCI will deploy tens of thousands more NVIDIA GPUs to its cloud service, including A100 and upcoming H100 accelerators. Read More
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