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Friday, May 23, 2025

Malaysia’s Huawei AI Reversal: A Spotlight on the US-China Tech Cold War

Key Takeaways

Malaysia’s flip-flop on Huawei’s AI servers highlights rising US-China tensions in tech.
The US warns against using Huawei’s Ascend chips globally, citing export control risks.
Southeast Asia is becoming a battleground for AI infrastructure dominance.

Malaysia’s AI Announcement—And Sudden U-Turn

On May 20, 2025, Malaysia’s Deputy Communications Minister Teo Nie Ching made headlines by announcing a national AI project powered by 3,000 Huawei Ascend GPU servers by 2026. The plan, developed with Chinese AI startup DeepSeek, aimed to position Malaysia as a leader in adopting Huawei’s AI infrastructure.

Why It Mattered:

Huawei’s Ascend chips are China’s answer to US-made Nvidia GPUs.
Southeast Asia is a $1 trillion digital economy by 2030 (Google-Temasek Report), making it critical for tech influence.

But within 24 hours, Malaysia’s government retracted the announcement, calling it a “private-sector initiative” unaffiliated with national policy. Huawei also denied selling Ascend chips in Malaysia.

Why Did Malaysia Backtrack? Pressure From the US

The reversal came after swift reactions from Washington:

US Export Control Warnings: Days earlier, the Commerce Department warned that using Huawei’s Ascend chips “anywhere in the world” could breach US sanctions.
Political Pressure: Trump’s AI advisor, David Sacks, called the deal proof of “China’s full tech stack” expansion, urging faster US AI exports.
Transshipment Concerns: Malaysia is under investigation for allegedly rerouting US chips to China—a violation of sanctions.

Malaysia’s Dilemma:

Balancing ties with China (its top trade partner) vs. US (a key investor in tech infrastructure).
Oracle and Microsoft are building data centers in Malaysia, relying on US-made chips.

The Bigger Picture: US vs. China in the AI Race

1. US Strategy: Flood Markets With Nvidia Chips

The Trump administration is racing to deploy American AI hardware (e.g., Nvidia H100 GPUs) in emerging markets like Southeast Asia and the Gulf. Recent deals include:

Saudi Arabia: 1 million+ advanced chips for AI projects.
UAE: A massive data center with US tech.

Goal: Lock in alliances before Chinese alternatives gain traction.

2. China’s Countermove: Huawei’s Ascend Chips

Despite US sanctions, Huawei’s Ascend GPUs are gaining ground:

Performance: Comparable to Nvidia’s A100 in AI tasks (SemiAnalysis report).
Cost: 20-30% cheaper than US alternatives.
Domestic Reliance: Used by Alibaba, Tencent, and Baidu in China.

What’s Next for Malaysia and the Region?

US Chip Rules 2.0: The Trump administration is drafting stricter export controls targeting Malaysia and Singapore to prevent chip diversion to China.
Data Center Boom: Malaysia’s AI infrastructure market is projected to grow 15% annually (IDC), attracting both US and Chinese firms.
Diplomatic Tightrope: Smaller nations face tough choices—aligning with US tech or China’s cost-efficient solutions.

Malaysia’s Huawei saga underscores how the US-China tech cold war is reshaping global AI infrastructure. For nations caught in the middle, the path forward requires balancing economic pragmatism with geopolitical risks. As AI becomes the backbone of modern economies, expect more countries to face similar dilemmas.

Sunday, March 23, 2025

NVIDIA Unveils Breakthrough Photonics Switches to Power Million-GPU AI Factories

NVIDIA Unveils Breakthrough Photonics Switches to Power Million-GPU AI Factories
Silicon photonics technology promises massive scalability, energy efficiency, and resilience for next-gen AI infrastructure.

At its annual GTC conference, NVIDIA announced a revolutionary leap in networking technology with NVIDIA Spectrum-X™ Photonics and Quantum-X Photonics, designed to connect millions of GPUs in AI factories. These co-packaged optics switches integrate cutting-edge silicon photonics—a fusion of electronics and light-based communication—to address the exploding demands of AI infrastructure.

Why This Matters

AI factories, the next generation of data centers, require unprecedented networking speeds, energy efficiency, and scalability to train trillion-parameter models. Traditional copper-based networks struggle with power consumption, signal degradation, and physical space constraints. NVIDIA’s photonics switches solve these challenges by:

1.6 Terabits per second (Tb/s) per port: Doubling current industry standards.
3.5x Energy Savings: Reducing power usage via integrated optics.
10x Resilience: Minimizing downtime in multi-tenant AI environments.
Scalability to Millions of GPUs: Enabling seamless communication across global AI clusters.

What Are Silicon Photonics?

Silicon photonics merges optical (light-based) and electronic components on a single chip. Unlike traditional networks that rely on separate transceivers and copper cables, NVIDIA’s approach integrates lasers, modulators, and detectors directly into switches. This eliminates bottlenecks, reduces latency, and cuts energy waste from converting electrical signals to light.

Key Innovations

Feature	Spectrum-X (Ethernet)	Quantum-X (InfiniBand)
Bandwidth	1.6 Tb/s per port	800 Gb/s per port
Port Configurations	Up to 2,048 ports (200Gb/s)	144 ports (800Gb/s)
Total Throughput	400 Tb/s	115 Tb/s
Energy Efficiency	3.5x better than traditional	5x higher scalability
Cooling	Air-cooled	Liquid-cooled
Availability	2026	Late 2024

Industry Collaboration

NVIDIA partnered with global leaders to build an end-to-end supply chain:

TSMC: Manufacturing advanced 3D-stacked chips using its SoIC (System on Integrated Chips) technology.
Corning: Supplying ultra-low-loss optical fibers like SMF-28® Ultra.
Foxconn: Scaling production of photonics-enabled switches and servers.
Coherent & Lumentum: Providing laser and modulator components.

These collaborations ensure cost-effective, high-volume production for AI factories.

Impact on AI Development

By 2026, Spectrum-X Ethernet switches will enable 400 Tb/s networks—enough to transfer 50,000 HD movies per second. Quantum-X InfiniBand, launching in late 2024, targets high-performance computing (HPC) clusters with liquid-cooled, low-latency designs. Together, they address two critical challenges:

Energy Costs: Data centers consume ~1% of global electricity; photonics cuts this dramatically.
Scalability: AI models like GPT-4 require months of training on thousands of GPUs. NVIDIA’s tech reduces this to weeks.

The Road Ahead

“AI factories will soon operate at planetary scale,” said NVIDIA CEO Jensen Huang. With partners like TSMC and Coherent, NVIDIA aims to redefine networking for generative AI, climate prediction, and autonomous systems.

For more details, watch the NVIDIA GTC 2024 keynote or explore technical sessions through March 21.

Learn more about:

Thursday, March 20, 2025

Introducing Newton: The Future of Robotics Simulation Made Simple

Meet Blue: The AI-Powered Robot

At the GTC 2025 AI conference, NVIDIA CEO Jensen Huang introduced Blue, an adorable AI-powered robot developed in collaboration with Disney Research and Google DeepMind. Inspired by Star Wars, Blue made its way onto the stage and engaged in a lively, real-time interaction with Huang.

“Hi Blue!” Huang greeted the robot, showcasing its advanced AI capabilities. Blue is powered by two NVIDIA computers housed within its compact frame, demonstrating how cutting-edge technology can bring robots to life.

“This is how we are going to train robots,” Huang explained, highlighting Blue’s role in showcasing the future of robotics. Blue is a perfect example of how Newton’s simulation technology can be used to create intelligent, interactive robots that feel almost human.

Introducing Newton: The Future of Robotics Simulation Made Simple

Imagine a world where robots can learn, adapt, and interact with their surroundings just like humans do. Sounds like science fiction, right? Well, thanks to Newton, a new open-source physics engine developed by NVIDIA, Google DeepMind, and Disney Research, this future is closer than ever.

Newton is designed to make robotics simulation faster, more accurate, and accessible to everyone—whether you’re a researcher, developer, or just someone curious about the future of robotics. Let’s break it down in simple terms.

Why Do We Need Robotics Simulation?

Before robots can be deployed in the real world, they need to be trained and tested. But testing robots in real-life scenarios can be expensive, time-consuming, and sometimes dangerous. That’s where simulation comes in.

Simulation allows developers to:

Train robots in virtual environments that mimic the real world.
Test algorithms safely without risking damage to hardware.
Speed up development by running multiple simulations at once.

However, there’s a catch. Many simulators struggle to perfectly replicate real-world physics, creating a gap known as the “sim-to-real” problem. Newton aims to solve this by offering a more realistic and flexible simulation platform.

What Makes Newton Special?

Newton isn’t just another physics engine—it’s a game-changer. Here’s why:

It’s Open Source
Newton is free to use, modify, and share. This means anyone, from big companies to individual developers, can use it to build and test their robots.
Powered by NVIDIA GPUs
Built on NVIDIA Warp, Newton uses the power of NVIDIA GPUs to run simulations at lightning speed. This makes it perfect for training complex AI models and running large-scale experiments.
Works with MuJoCo-Warp
Newton integrates seamlessly with MuJoCo-Warp, a high-performance simulator developed by Google DeepMind. This integration allows developers to achieve incredible speedups—up to 100x faster for tasks like in-hand manipulation.
Differentiable Physics
Newton supports differentiable simulations, a fancy term for its ability to calculate gradients for optimization. This makes it easier to train robots using machine learning techniques.
Highly Customizable
Whether you’re simulating rigid objects, soft materials, or even complex interactions like sand or cloth, Newton can handle it. Developers can also add custom solvers to simulate unique behaviors.
Built on OpenUSD
Newton uses OpenUSD (Universal Scene Description), a framework that makes it easy to create detailed and realistic environments. Think of it as a universal language for describing robots, objects, and their interactions.

Real-World Applications

Newton isn’t just for researchers—it’s already being used to create real-world innovations:

Disney Research is using Newton to develop next-generation entertainment robots, like the Star Wars-inspired BDX droids. These robots are designed to be more expressive and interactive, bringing characters to life in ways we’ve never seen before.
Google DeepMind is leveraging Newton to advance its robotics research, particularly in areas like humanoid locomotion and dexterous manipulation.

A Collaborative Effort

Newton is the result of a unique collaboration between NVIDIA, Google DeepMind, and Disney Research. Together, these organizations are setting a new standard for robotics simulation.

They’re also working on an OpenUSD asset structure for robotics, which will make it easier to share and reuse robotic models and data. This means developers won’t have to start from scratch every time they build a new robot.

What’s Next for Newton?

The first version of Newton is expected to be released later this year. In the meantime, developers can explore the technologies behind it:

NVIDIA Warp: A framework for GPU-accelerated simulations.
NVIDIA Isaac Lab: An open-source platform for robot learning.

Why Should You Care?

Newton isn’t just for robotics experts—it’s for anyone excited about the future of technology. Whether you’re a student, a hobbyist, or a professional developer, Newton offers the tools you need to bring your ideas to life.

So, get ready to dive into the world of robotics simulation. With Newton, the future is in your hands.

For more information, visit the official NVIDIA Robotics page.

NVIDIA DGX B300: Full Specification Revealed

Hardware:

GPUs: NVIDIA Blackwell Ultra GPUs (learn more about NVIDIA Blackwell GPUs) with 2.3TB total memory for massive datasets.
CPU: Dual Intel® Xeon® Processors (learn more about Intel Xeon Processors).
Performance: 72 PFLOPS FP8 (training) and 144 PFLOPS FP4 (inference).
Networking: 800Gb/s InfiniBand/Ethernet, 8x OSFP ports, and NVIDIA BlueField-3 DPUs for ultra-fast data transfer.
Storage: 38TB OS NVMe + 30.7TB internal NVMe (learn more about NVMe) for high-speed data access.
Power: ~14kW consumption (typical for high-end AI systems).

Software & Support:

Runs NVIDIA DGX OS (learn more about NVIDIA DGX Systems), NVIDIA Mission Control, and supports Linux OS options like Red Hat Enterprise Linux, Rocky Linux, and Ubuntu.
Includes 3-year support for reliability.

Ideal for enterprises tackling complex AI workloads, offering scalability, speed, and robust management tools.

Key AI Industry Context:

The NVIDIA DGX B300 is part of NVIDIA's ongoing innovation in AI hardware, following the success of previous systems like the NVIDIA DGX A100. Its release aligns with the growing demand for high-performance computing in AI, driven by advancements in deep learning and machine learning. NVIDIA's GPUs have been instrumental in powering breakthroughs like GPT-3 and other large language models (LLMs), making systems like the DGX B300 critical for AI research and development.

For more details on NVIDIA's AI ecosystem, visit NVIDIA AI Enterprise and NVIDIA Base Command Manager.

For More Information:
To explore the full capabilities and specifications of the NVIDIA DGX B300, please refer to the official NVIDIA DGX B300 product page. Additionally, you can learn more about NVIDIA's broader AI and data center solutions by visiting the NVIDIA Data Center Solutions website. For the latest updates on NVIDIA's AI advancements and industry events, check out the NVIDIA Newsroom.

NVIDIA’s New Blackwell Ultra: Supercharging AI to Think and Solve Problems Like Never Before

At its annual GTC 2025 conference, NVIDIA announced Blackwell Ultra—a groundbreaking AI platform designed to power advanced AI reasoning. Here’s a simplified breakdown of what this means and why it matters.

What Is Blackwell Ultra?

Blackwell Ultra is NVIDIA’s latest AI platform, acting like a supercharged engine for two key tasks:

Training AI: Teaching models using massive datasets.
AI Reasoning: Enabling AI to solve problems step-by-step, akin to human logic.

This marks a shift from AI that learns (e.g., recognizing patterns) to AI that thinks (e.g., planning, analyzing).

Why Is This Important?

1. Smarter AI Assistants and Robots
Blackwell Ultra supports agentic AI (AI that acts autonomously). Imagine:

Logistics AI rerouting deliveries during a storm.
Robots fixing machinery without human input.

2. Faster, More Accurate Responses
The HGX B300 NVL16 system delivers 11x faster inference for large language models (LLMs) like Llama Nemotron Reason, improving chatbots and medical AIs.

3. Cost Efficiency
NVIDIA claims Blackwell Ultra reduces costs while boosting performance, democratizing access to advanced AI.

How Does It Work?

The platform includes two key products:

GB300 NVL72: A rack-scale “AI factory” combining 72 Blackwell GPUs and 36 Grace CPUs for massive computing power.
HGX B300 NVL16: A compact system optimized for trillion-parameter models.

Both leverage NVIDIA’s Blackwell architecture, offering 4x more memory than the prior Hopper generation.

Cool Uses for Blackwell Ultra

Physical AI: Generate photorealistic 3D worlds with tools like NVIDIA Omniverse to train robots and self-driving cars.
Cloud AI: Faster, cheaper services on AWS, Google Cloud, and Microsoft Azure.

Networking and Software Upgrades

Spectrum-X 800G Ethernet: NVIDIA’s high-speed networking platform reduces delays for distributed AI.
Dynamo Software: Open-source inference framework that splits tasks across GPUs to cut costs.

When Can You Use It?

Starting late 2025, partners like Dell, HPE, and Lenovo will offer Blackwell Ultra systems. Cloud providers like CoreWeave and Lambda will host instances.

Why Should You Care?

Better Everyday AI: Smarter chatbots, faster translations, reliable self-driving tech.
Cheaper Innovation: Startups can leverage NVIDIA AI Enterprise for scalable solutions.
Future Tech: Foundations for AI scientists, advanced robotics, and more.

The Bottom Line

Blackwell Ultra isn’t just an upgrade—it’s a leap toward AI that thinks and solves problems. Dive deeper by watching NVIDIA’s GTC 2024 keynote.

For developers, explore tools like CUDA-X libraries and NIM microservices to build next-gen AI.

Saturday, March 8, 2025

NVIDIA Blackwell Architecture: Redefining the Future of AI and Accelerated Computing

NVIDIA Blackwell Architecture: Redefining the Future of AI and Accelerated Computing

NVIDIA has once again pushed the boundaries of technology with the introduction of the Blackwell architecture, a groundbreaking platform designed to revolutionize generative AI and accelerated computing. Named after the renowned mathematician David Blackwell, this new architecture promises unparalleled performance, efficiency, and scalability, setting the stage for the next era of AI innovation. Let’s break down what makes Blackwell a game-changer in simple, easy-to-understand terms.

A New Class of AI Superchip

At the heart of the Blackwell architecture is a massive AI superchip packed with 208 billion transistors, manufactured using TSMC’s cutting-edge 4NP process. What makes Blackwell unique is its dual-die design, where two reticle-limited dies are connected by a 10 TB/s chip-to-chip interconnect. This creates a unified GPU that delivers unprecedented computing power, making it ideal for handling the most demanding AI workloads.

Second-Generation Transformer Engine: Smarter and Faster AI

Blackwell introduces the second-generation Transformer Engine, a specialized component designed to accelerate AI training and inference for large language models (LLMs) and Mixture-of-Experts (MoE) models.

Micro-Tensor Scaling: This innovative technique allows Blackwell to optimize performance and accuracy using 4-bit floating point (FP4) precision, doubling the speed and efficiency of AI models while maintaining high accuracy.
Community-Defined Formats: Blackwell supports new microscaling formats, making it easier for developers to replace larger precisions without sacrificing performance.

In simpler terms, Blackwell makes AI models faster, smarter, and more efficient, enabling breakthroughs in fields like natural language processing, image generation, and scientific research.

Secure AI: Protecting Your Data and Models

With great power comes great responsibility, and Blackwell takes AI security to the next level. It features NVIDIA Confidential Computing, a hardware-based security system that protects sensitive data and AI models from unauthorized access.

TEE-I/O Capability: Blackwell is the first GPU to support Trusted Execution Environment Input/Output (TEE-I/O), ensuring secure communication between GPUs and hosts.
Near-Zero Performance Loss: Despite the added security, Blackwell delivers nearly identical performance compared to unencrypted modes, making it ideal for enterprises handling sensitive data.

Whether you’re training AI models or running federated learning, Blackwell ensures your data and intellectual property are safe.

NVLink and NVLink Switch: Scaling AI to New Heights

One of the biggest challenges in AI is scaling models across multiple GPUs. Blackwell solves this with the fifth-generation NVLink and NVLink Switch Chip.

576 GPUs Connected: NVLink can scale up to 576 GPUs, enabling seamless communication for trillion-parameter AI models.
130 TB/s Bandwidth: The NVLink Switch Chip delivers 130 TB/s of GPU bandwidth, making it 4X more efficient than previous generations.
Multi-Server Clusters: Blackwell supports multi-server clusters, allowing 9X more GPU throughput than traditional eight-GPU systems.

This means faster training times, larger AI models, and more efficient data processing for industries like healthcare, finance, and autonomous driving.

Decompression Engine: Accelerating Data Analytics

Data is the lifeblood of AI, and Blackwell makes processing it faster and more efficient. The Decompression Engine accelerates data analytics workflows by offloading tasks traditionally handled by CPUs.

900 GB/s Bandwidth: Blackwell connects to the NVIDIA Grace CPU with a 900 GB/s link, enabling rapid access to massive datasets.
Support for Modern Formats: It supports popular compression formats like LZ4, Snappy, and Deflate, speeding up database queries and analytics pipelines.

For data scientists and analysts, this means faster insights and lower costs.

Reliability, Availability, and Serviceability (RAS): Smarter Resilience

Blackwell introduces a dedicated RAS Engine to ensure systems run smoothly and efficiently.

Predictive Management: NVIDIA’s AI-powered tools monitor thousands of data points to predict and prevent potential failures.
Faster Troubleshooting: The RAS Engine provides detailed diagnostics, helping engineers quickly identify and fix issues.
Minimized Downtime: By catching problems early, Blackwell reduces downtime, saving time, energy, and money.

This makes Blackwell not just powerful but also reliable, ensuring continuous operation for mission-critical applications.

Why Blackwell Matters

The NVIDIA Blackwell architecture is more than just a technological leap—it’s a foundation for the future of AI and computing. Here’s why it matters:

Unmatched Performance: With 208 billion transistors and 10 TB/s interconnects, Blackwell delivers the power needed for next-gen AI models.
Efficiency: Features like micro-tensor scaling and FP4 precision make AI faster and more resource-efficient.
Scalability: NVLink and NVLink Switch enable trillion-parameter models and multi-server clusters.
Security: Confidential Computing ensures data and models are protected without sacrificing performance.
Reliability: The RAS Engine minimizes downtime and maximizes efficiency.

Conclusion: The Future Starts with Blackwell

The NVIDIA Blackwell architecture is a game-changer for AI and accelerated computing. Whether you’re a researcher pushing the boundaries of generative AI, a data scientist analyzing massive datasets, or an enterprise building secure AI solutions, Blackwell provides the tools you need to succeed.

With its unprecedented performance, innovative features, and scalability, Blackwell is not just a step forward—it’s a giant leap into the future of technology.

Welcome to the era of Blackwell. Welcome to the future of AI.

Disclaimer

While every effort has been made to ensure the accuracy of the information provided in this article, specifications and features are subject to change based on official updates from NVIDIA. For the most accurate and up-to-date information, please refer to the official NVIDIA website or contact their customer support. This article is intended for informational purposes only and should not be considered as an official statement from NVIDIA.

NVIDIA DLSS 4: The Ultimate Gaming Upgrade – Multi Frame Generation, Transformer AI, and 8X Performance Boost

NVIDIA has once again pushed the boundaries of gaming technology with the introduction of DLSS 4 at CES 2025. This latest iteration of Deep Learning Super Sampling (DLSS) brings groundbreaking advancements in performance, image quality, and efficiency, particularly for the upcoming GeForce RTX 50 Series GPUs. Here are the key points from the announcement:

Multi Frame Generation:

Generates up to three additional frames per traditionally rendered frame.
Boosts frame rates by up to 8X over traditional rendering.
Enables 4K 240 FPS fully ray-traced gaming on the GeForce RTX 5090.

Transformer-Based AI Models:

Replaces Convolutional Neural Networks (CNNs) with transformer-based models, the same architecture used in AI systems like ChatGPT.
Improves temporal stability, reduces ghosting, and enhances detail in motion.

Performance and Efficiency:

40% faster AI model with 30% less VRAM usage.
5th Generation Tensor Cores with 2.5X more AI processing power.
Hardware Flip Metering for smoother frame pacing.

Upgrades for All RTX GPUs:

DLSS Ray Reconstruction, Super Resolution, and DLAA receive transformer-based upgrades.
Frame Generation improvements for both RTX 40 Series and RTX 50 Series GPUs.

Image Quality Improvements:

Enhanced Ray Reconstruction for better lighting and reflections in ray-traced games.
Super Resolution beta release for testing and feedback.

Game Support:

75 games and apps will support Multi Frame Generation at launch.

NVIDIA has once again redefined the gaming landscape with the introduction of DLSS 4, the latest evolution of its Deep Learning Super Sampling technology. Designed exclusively for the upcoming GeForce RTX 50 Series GPUs, DLSS 4 brings Multi Frame Generation, transformer-based AI models, and a host of performance and image quality enhancements that promise to revolutionize gaming. Let’s dive into what makes DLSS 4 a game-changer for gamers and developers alike.

Multi Frame Generation: A Quantum Leap in Performance

The star of DLSS 4 is Multi Frame Generation, a groundbreaking feature that generates up to three additional frames for every traditionally rendered frame. This innovation works in tandem with existing DLSS technologies to deliver up to 8X faster frame rates compared to traditional rendering methods.

For gamers, this means 4K gaming at 240 FPS with full ray tracing enabled—a feat previously unimaginable. Titles like Warhammer 40,000: Darktide have already demonstrated a 10% performance boost and 400MB less VRAM usage at 4K max settings, showcasing the efficiency of DLSS 4.

Transformer-Based AI Models: Smarter, Faster, Better

DLSS 4 introduces the first real-time application of transformer-based AI models in gaming. These models, which power advanced AI systems like ChatGPT, replace the older Convolutional Neural Networks (CNNs) used in previous DLSS versions.

The result? Improved temporal stability, reduced ghosting, and higher detail in motion. For example, in Alan Wake 2, the new transformer model eliminates shimmering on power lines, reduces ghosting on fan blades, and enhances the stability of chainlink fences, creating a more immersive gaming experience.

Performance and Efficiency: Doing More with Less

DLSS 4 isn’t just about raw power—it’s also about efficiency. The new AI model is 40% faster and uses 30% less VRAM, making it ideal for high-performance gaming without compromising system resources.

The 5th Generation Tensor Cores in the RTX 50 Series GPUs provide 2.5X more AI processing power, enabling the GPU to handle five AI models simultaneously (Super Resolution, Ray Reconstruction, and Multi Frame Generation) within milliseconds.

Additionally, Hardware Flip Metering ensures smoother frame pacing by shifting timing logic to the display engine, eliminating the inconsistencies seen in DLSS 3.

Upgrades for All RTX Gamers

While Multi Frame Generation is exclusive to the RTX 50 Series, NVIDIA hasn’t forgotten its existing users. DLSS 4 brings transformer-based upgrades to DLSS Ray Reconstruction, Super Resolution, and DLAA for all RTX GPUs.

Gamers on the RTX 40 Series will also benefit from improved Frame Generation, which boosts performance while reducing VRAM usage.

Image Quality: A Visual Feast

DLSS 4 doesn’t just make games faster—it makes them look better. The new Ray Reconstruction model delivers stunning lighting and reflections in ray-traced games, while the Super Resolution beta offers better temporal stability and higher detail in motion.

For example, in Cyberpunk 2077, the transformer model enhances the clarity of neon lights and reduces artifacts in fast-moving scenes, making Night City more vibrant and lifelike than ever.

Game Support: A Growing Ecosystem

At launch, 75 games and apps will support Multi Frame Generation, with more titles expected to join the list. Popular games like Call of Duty, Assassin’s Creed, and Starfield are already confirmed to receive DLSS 4 upgrades, ensuring gamers have plenty of options to experience the technology.

Conclusion: The Future of Gaming Starts Now

NVIDIA DLSS 4 is more than just an upgrade—it’s a paradigm shift in gaming technology. With Multi Frame Generation, transformer-based AI models, and unprecedented performance and efficiency, DLSS 4 sets a new standard for what’s possible in gaming.

Whether you’re a competitive gamer chasing 240 FPS or a casual player seeking stunning visuals, DLSS 4 delivers. And with support for all RTX GPUs, NVIDIA ensures that no one is left behind in this new era of gaming.

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