Stability AI, a maker of various generative AI models and the company behind text-to-image Stable Diffusion models, has released its latest Stable Diffusion 3 (SD3) Medium AI model. Running on two billion dense parameters, the SD3 Medium is the company's most advanced text-to-image model to date. It boasts features like generating highly realistic and detailed images across a wide range of styles and compositions. It demonstrates capabilities in handling intricate prompts that involve spatial reasoning, actions, and diverse artistic directions. The model's innovative architecture, including the 16-channel variational autoencoder (VAE), allows it to overcome common challenges faced by other models, such as accurately rendering realistic human faces and hands.

Additionally, it achieves exceptional text quality, with precise letter formation, kerning, and spacing, thanks to the Diffusion Transformer architecture. Notably, the model is resource-efficient, capable of running smoothly on consumer-grade GPUs without compromising performance due to its low VRAM footprint. Furthermore, it exhibits impressive fine-tuning abilities, allowing it to absorb and replicate nuanced details from small datasets, making it highly customizable for specific use cases that users may have. Being an open-weight model, it is available for download on HuggingFace, and it has libraries optimized for both NVIDIA's TensorRT (all modern NVIDIA GPUs) and AMD Radeon/Instinct GPUs.

AAEON, a leading designer and manufacturer of AI Edge solutions, has today confirmed that its Fanless Embedded AI System, the BOXER-8640AI, will support the newly released NVIDIA Jetpack 6.0 software development kit.

The NVIDIA Jetpack 6.0 SDK is equipped with a range of new features conducive to accelerating AI application development, such as Jetson Platform Services, a modular, API-driven suite of pre-built and cloud-native software services.

Last week, we got confirmation from the highest levels of Dell and NVIDIA that the latter is making a client PC processor for the Windows on Arm (WoA) AI PC ecosystem that only has one player in it currently, Qualcomm. Michael Dell hinted that this NVIDIA AI PC processor would be ready in 2025. Since then, speculation has been rife about the various IP blocks NVIDIA could use in the development of this chip, the two key areas of debate have been the CPU cores and the process node.

Given that NVIDIA is gunning toward a 2025 launch of its AI PC processor, the company could implement reference Arm IP CPU cores, such as the Arm Cortex X5 "Blackhawk," and not venture out toward developing its own CPU cores on the Arm machine architecture, unlike Apple. Depending on how the market recieves its chips, NVIDIA could eventually develop its own cores. Next up, the company could use the most advanced 3 nm-class foundry node available in 2025 for its chip, such as the TSMC N3P. Given that even Apple and Qualcomm will build their contemporary notebook chips on this node, it would be a logical choice of node for NVIDIA. Then there's graphics and AI acceleration hardware.

NVIDIA is eying a comeback to the client processor business, reveals a Bloomberg interview with the CEOs of NVIDIA and Dell. For NVIDIA, all it takes is a simple driver update that exposes every GeForce GPU with tensor cores as an NPU to Windows 11, with translation layers to get popular client AI apps to work with TensorRT. But that would need you to have a discrete NVIDIA GPU. What about the vast market of Windows AI PCs powered by the likes of Qualcomm, Intel, and AMD, who each sell 15 W-class processors with integrated NPUs capable of 50 AI TOPS, which is all that Copilot+ needs? NVIDIA held an Arm license for decades now, and makes Arm-based CPUs to this day, with the NVIDIA Grace, however, that is a large server processor meant for its AI GPU servers.

NVIDIA already made client processors under the Tegra brand targeting smartphones, which it winded down last decade. It's since been making Drive PX processors for its automotive self-driving hardware division; and of course there's Grace. NVIDIA hinted that it might have a client CPU for the AI PC market in 2025. In the interview Bloomberg asked NVIDIA CEO Jensen Huang a pointed question on whether NVIDIA has a place in the AI PC market. Dell CEO Michael Dell, who was also in the interview, interjected "come back next year," to which Jensen affirmed "exactly." Dell would be in a front-and-center position to know if NVIDIA is working on a new PC processor for launch in 2025, and Jensen's nod almost confirms this

NVIDIA today announced at Microsoft Build new AI performance optimizations and integrations for Windows that help deliver maximum performance on NVIDIA GeForce RTX AI PCs and NVIDIA RTX workstations. Large language models (LLMs) power some of the most exciting new use cases in generative AI and now run up to 3x faster with ONNX Runtime (ORT) and DirectML using the new NVIDIA R555 Game Ready Driver. ORT and DirectML are high-performance tools used to run AI models locally on Windows PCs.

WebNN, an application programming interface for web developers to deploy AI models, is now accelerated with RTX via DirectML, enabling web apps to incorporate fast, AI-powered capabilities. And PyTorch will support DirectML execution backends, enabling Windows developers to train and infer complex AI models on Windows natively. NVIDIA and Microsoft are collaborating to scale performance on RTX GPUs. These advancements build on NVIDIA's world-leading AI platform, which accelerates more than 500 applications and games on over 100 million RTX AI PCs and workstations worldwide.

It's official: NVIDIA delivered the world's fastest platform in industry-standard tests for inference on generative AI. In the latest MLPerf benchmarks, NVIDIA TensorRT-LLM—software that speeds and simplifies the complex job of inference on large language models—boosted the performance of NVIDIA Hopper architecture GPUs on the GPT-J LLM nearly 3x over their results just six months ago. The dramatic speedup demonstrates the power of NVIDIA's full-stack platform of chips, systems and software to handle the demanding requirements of running generative AI. Leading companies are using TensorRT-LLM to optimize their models. And NVIDIA NIM—a set of inference microservices that includes inferencing engines like TensorRT-LLM—makes it easier than ever for businesses to deploy NVIDIA's inference platform.

Raising the Bar in Generative AI
TensorRT-LLM running on NVIDIA H200 Tensor Core GPUs—the latest, memory-enhanced Hopper GPUs—delivered the fastest performance running inference in MLPerf's biggest test of generative AI to date. The new benchmark uses the largest version of Llama 2, a state-of-the-art large language model packing 70 billion parameters. The model is more than 10x larger than the GPT-J LLM first used in the September benchmarks. The memory-enhanced H200 GPUs, in their MLPerf debut, used TensorRT-LLM to produce up to 31,000 tokens/second, a record on MLPerf's Llama 2 benchmark. The H200 GPU results include up to 14% gains from a custom thermal solution. It's one example of innovations beyond standard air cooling that systems builders are applying to their NVIDIA MGX designs to take the performance of Hopper GPUs to new heights.

We're excited to announce we're expanding our AI Inference benchmark offerings with the UL Procyon AI Image Generation Benchmark, coming Monday, 25th March. AI has the potential to be one of the most significant new technologies hitting the mainstream this decade, and many industry leaders are competing to deliver the best AI Inference performance through their hardware. Last year, we launched the first of our Procyon AI Inference Benchmarks for Windows, which measured AI Inference performance with a workload using Computer Vision.

The upcoming UL Procyon AI Image Generation Benchmark provides a consistent, accurate and understandable workload for measuring the AI performance of high-end hardware, built with input from members of the industry to ensure fair and comparable results across all supported hardware.

At GTC on Monday, Microsoft Corp. and NVIDIA expanded their longstanding collaboration with powerful new integrations that leverage the latest NVIDIA generative AI and Omniverse technologies across Microsoft Azure, Azure AI services, Microsoft Fabric and Microsoft 365.

"Together with NVIDIA, we are making the promise of AI real, helping to drive new benefits and productivity gains for people and organizations everywhere," said Satya Nadella, Chairman and CEO, Microsoft. "From bringing the GB200 Grace Blackwell processor to Azure, to new integrations between DGX Cloud and Microsoft Fabric, the announcements we are making today will ensure customers have the most comprehensive platforms and tools across every layer of the Copilot stack, from silicon to software, to build their own breakthrough AI capability."

"AI is transforming our daily lives - opening up a world of new opportunities," said Jensen Huang, founder and CEO of NVIDIA. "Through our collaboration with Microsoft, we're building a future that unlocks the promise of AI for customers, helping them deliver innovative solutions to the world."

Stability AI, the developers behind the popular Stable Diffusion generative AI model, have run some first-party performance benchmarks for Stable Diffusion 3 using popular data-center AI GPUs, including the NVIDIA H100 "Hopper" 80 GB, A100 "Ampere" 80 GB, and Intel's Gaudi2 96 GB accelerator. Unlike the H100, which is a super-scalar CUDA+Tensor core GPU; the Gaudi2 is purpose-built to accelerate generative AI and LLMs. Stability AI published its performance findings in a blog post, which reveals that the Intel Gaudi2 96 GB is posting a roughly 56% higher performance than the H100 80 GB.

With 2 nodes, 16 accelerators, and a constant batch size of 16 per accelerator (256 in all), the Intel Gaudi2 array is able to generate 927 images per second, compared to 595 images for the H100 array, and 381 images per second for the A100 array, keeping accelerator and node counts constant. Scaling things up a notch to 32 nodes, and 256 accelerators or a batch size of 16 per accelerator (total batch size of 4,096), the Gaudi2 array is posting 12,654 images per second; or 49.4 images per-second per-device; compared to 3,992 images per second or 15.6 images per-second per-device for the older-gen A100 "Ampere" array.

Generative AI is driving change across industries—and to take advantage of its benefits, businesses must select the right hardware to power their workflows. The new NVIDIA RTX 2000 Ada Generation GPU delivers the latest AI, graphics and compute technology to compact workstations, offering up to 1.5x the performance of the previous-generation RTX A2000 12 GB in professional workflows. From crafting stunning 3D environments to streamlining complex design reviews to refining industrial designs, the card's capabilities pave the way for an AI-accelerated future, empowering professionals to achieve more without compromising on performance or capabilities. Modern multi-application workflows, such as AI-powered tools, multi-display setups and high-resolution content, put significant demands on GPU memory. With 16 GB of memory in the RTX 2000 Ada, professionals can tap the latest technologies and tools to work faster and better with their data.

Powered by NVIDIA RTX technology, the new GPU delivers impressive realism in graphics with NVIDIA DLSS, delivering ultra-high-quality, photorealistic ray-traced images more than 3x faster than before. In addition, the RTX 2000 Ada enables an immersive experience for enterprise virtual-reality workflows, such as for product design and engineering design reviews. With its blend of performance, versatility and AI capabilities, the RTX 2000 Ada helps professionals across industries achieve efficiencies. Architects and urban planners can use it to accelerate visualization workflows and structural analysis, enhancing design precision. Product designers and engineers using industrial PCs can iterate rapidly on product designs with fast, photorealistic rendering and AI-powered generative design. Content creators can edit high-resolution videos and images seamlessly, and use AI for realistic visual effects and content creation assistance. And in vital embedded applications and edge computing, the RTX 2000 Ada can power real-time data processing for medical devices, optimize manufacturing processes with predictive maintenance and enable AI-driven intelligence in retail environments.

Today, Dropbox, Inc. and NVIDIA announced a collaboration to supercharge knowledge work and improve productivity for millions of Dropbox customers through the power of AI. The companies' collaboration will expand Dropbox's extensive AI functionality with new uses for personalized generative AI to improve search accuracy, provide better organization, and simplify workflows for its customers across their cloud content.

Dropbox plans to leverage NVIDIA's AI foundry consisting of NVIDIA AI Foundation Models, NVIDIA AI Enterprise software and NVIDIA accelerated computing to enhance its latest AI-powered product experiences. These include Dropbox Dash, universal search that connects apps, tools, and content in a single search bar to help customers find what they need; Dropbox AI, a tool that allows customers to ask questions and get summaries on large files across their entire Dropbox; among other AI capabilities in Dropbox.

Even as CPU vendors are working to mainstream accelerated AI for client PCs, and Microsoft setting the pace for more AI in everyday applications with Windows 11 23H2 Update; NVIDIA is out there reminding you that every GeForce RTX GPU is an AI accelerator. This is thanks to its Tensor cores, and the SIMD muscle of the ubiquitous CUDA cores. NVIDIA has been making these for over 5 years now, and has an install base of over 100 million. The company is hence focusing on bring generative AI acceleration to more client- and enthusiast relevant use-cases, such as large language models.

NVIDIA at the Microsoft Ignite event announced new optimizations, models, and resources to bring accelerated AI to everyone with an NVIDIA GPU that meets the hardware requirements. To begin with, the company introduced an update to TensorRT-LLM for Windows, a library that leverages NVIDIA RTX architecture for accelerating large language models (LLMs). The new TensorRT-LLM version 0.6.0 will release later this month, and improve LLM inference performance by up to 5 times in terms of tokens per second, when compared to the initial release of TensorRT-LLM from October 2023. In addition, TensorRT-LLM 0.6.0 will introduce support for popular LLMs, including Mistral 7B and Nemtron-3 8B. Accelerating these two will require a GeForce RTX 30-series "Ampere" or 40-series "Ada" GPU with at least 8 GB of main memory.