NVIDIA Propels 100 Million Windows RTX PCs and Workstations into an Era of Generative Power

NVIDIA has made significant strides in accelerating the development and deployment of generative AI models, bringing groundbreaking performance enhancements. With the integration of Tensor Cores and upcoming Max-Q low-power AI, NVIDIA propels 100 million Windows RTX PCs and Workstations into an era of generative power. This advancement is set to shape the future of productivity, content creation, and gaming.

NVIDIA Propels 100 Million Windows RTX PCs and Workstations into an Era of Generative Power

Revolutionizing Generative AI on Windows RTX PCs and Workstations

  • Generative AI, powered by neural networks, is revolutionizing industries by creating new and original content.
  • NVIDIA’s RTX GPUs are utilized by powerful generative AI models like NVIDIA NeMo, DLSS 3 Frame Generation, Meta LLaMa, ChatGPT, Adobe Firefly, and Stable Diffusion.
  • Optimization of these models for GeForce RTX and NVIDIA RTX GPUs enables developers to achieve performance up to 5 times faster compared to competing devices.
  • Tensor Cores, dedicated hardware in RTX GPUs, play a crucial role in accelerating AI calculations, contributing to the impressive speed boost.
  • Recent software enhancements unveiled at the Microsoft Build conference have doubled the performance of generative AI models, such as Stable Diffusion, thanks to new DirectML optimizations.

Max-Q Low-Power AI Inferencing for Enhanced Efficiency

As AI inferencing increasingly occurs on local devices, the demand for efficient hardware becomes crucial. To meet this need, NVIDIA is introducing Max-Q low-power inferencing for AI workloads on RTX GPUs. NVIDIA’s Max-Q low-power inferencing for AI workloads on RTX GPUs offers several advantages, providing an optimized balance between power consumption and performance. Here are the key points:

1. Efficient Hardware Demand: As AI inferencing takes place more frequently on local devices, there is a growing need for efficient hardware to support these tasks.

2. Introducing Max-Q Low-Power Inferencing: NVIDIA is addressing this demand by introducing Max-Q low-power inferencing for AI workloads on RTX GPUs.

3. Power Optimization: Max-Q allows GPUs to operate at a fraction of their power capacity for lighter inferencing tasks. This results in reduced power consumption and improved energy efficiency.

4. Unparalleled Performance: Despite operating at lower power levels for lighter tasks, RTX GPUs equipped with Max-Q still deliver exceptional performance. They can handle resource-intensive generative AI workloads efficiently, ensuring high-quality results.

5. Optimized Balance: The key advantage of Max-Q is the optimized balance it achieves between power consumption and performance. It enables PCs and workstations to handle complex AI tasks effectively while conserving energy.

6. Enhanced AI Capabilities: With Max-Q, users can experience the full potential of AI inferencing on their devices without compromising performance or power efficiency.

7. Enabling AI Everywhere: Max-Q extends the reach of AI inferencing, allowing it to happen on local devices without sacrificing efficiency. This empowers users to leverage AI capabilities wherever they need them.

8. Improved User Experience: By providing efficient hardware for AI inferencing, Max-Q contributes to a better user experience. Users can enjoy faster and more responsive AI applications, creating a seamless and efficient computing environment.

NVIDIA’s Max-Q low-power inferencing technology on RTX GPUs revolutionizes the efficiency and performance of AI workloads on PCs and workstations. It enables devices to handle complex AI tasks with minimal power consumption, ensuring optimal performance and an enhanced user experience.

Complete RTX-Accelerated AI Development Stack

Developers now have access to a comprehensive RTX-accelerated AI development stack running on Windows 11, simplifying the process of developing, training and deploying advanced AI models. Here are the key points:

  • Model Development and Fine-Tuning: Developers can begin model development and fine-tuning using optimized deep learning frameworks available via Windows Subsystem for Linux.
  • Transition to Cloud Training: Developers can seamlessly transition to the cloud for training their AI models. The same NVIDIA AI stack is available through major cloud service providers, ensuring consistency and compatibility throughout the development process.
  • Training with NVIDIA AI Stack: Cloud-based training using the NVIDIA AI stack offers enhanced performance and scalability. Developers can leverage the power of NVIDIA RTX GPUs for faster and more efficient training of their AI models.
  • Optimization for Fast Inferencing: Once the models are trained, developers can optimize them for fast inferencing. Tools like Microsoft Olive can be utilized to fine-tune the models for optimal performance during inferencing tasks.
  • Deployment to RTX PCs and Workstations: The AI-enabled applications and features can be deployed to a vast install base of over 100 million RTX PCs and workstations. These devices have been meticulously optimized for AI performance, ensuring the smooth and efficient execution of AI applications.

NVIDIA’s Commitment to Transformative AI Experiences

With over 400 RTX AI-accelerated apps and games already released, NVIDIA continues to drive innovation across industries. During his keynote address at COMPUTEX 2023, NVIDIA founder and CEO Jensen Huang introduced NVIDIA Avatar Cloud Engine (ACE) for Games, a new generative AI model foundry service. ACE for Games empowers developers to bring intelligence to non-playable characters through AI-powered natural language interactions.

Middleware developers, game creators, and tool developers can utilize ACE for Games to build and deploy customized speech, conversation, and animation AI models, transforming the gaming experience.

The Future of Generative AI on RTX

Generative AI on RTX GPUs is not limited to specific devices or platforms; it spans servers, the cloud, and local devices. NVIDIA’s dedication to AI computing has led to optimized hardware and software architecture, including fourth-generation Tensor Cores on RTX GPUs.

Regular driver optimizations ensure peak performance, with the most recent NVIDIA driver and Olive-optimized models delivering significant speedups for developers on Windows 11.

Additionally, the latest generation of RTX laptops and mobile workstations built on the NVIDIA Ada Lovelace architecture offers unprecedented performance and portability. Leading manufacturers like Dell, HP, Lenovo, and ASUS are propelling the generative AI era forward with their RTX GPU-powered devices.

As NVIDIA propels 100 million Windows RTX PCs and Workstations into an era of generative power, NVIDIA’s collaborative efforts with Microsoft and hardware partners ensure that developers and users can fully harness the transformative power of AI across various domains.

Source: Nvidia Newsroom

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