DeepSeek-R1 Launches on NVIDIA’s NIM Platform for Enhanced AI Capabilities

Introducing DeepSeek-R1: The Future of Reasoning Models

If you’ve ever wondered how AI can think like us, then let me introduce you to DeepSeek-R1. This isn’t just any model; it’s an open model that’s redefining how we approach reasoning in artificial intelligence. Unlike traditional models that spit out answers directly, DeepSeek-R1 takes a more nuanced approach. It performs multiple inference passes over a query, engaging in a kind of mental gymnastics that involves chain-of-thought, consensus, and search methods to arrive at the best answer. This clever process is what we call test-time scaling.

But why does this matter? Well, as DeepSeek-R1 iteratively “thinks” through problems, it generates more output tokens and extends its generation cycles. This means better quality responses over time. The key takeaway here is that significant test-time compute is essential for real-time inference and high-quality responses from models like DeepSeek-R1. It’s a perfect example of why accelerated computing is becoming a cornerstone for agentic AI inference.

What Makes DeepSeek-R1 Stand Out?

DeepSeek-R1 is not just big; it’s massive! With a whopping 671 billion parameters—yes, you read that right—it’s ten times larger than many popular open-source language models. This allows it to handle a substantial input context length of 128,000 tokens. But what does that mean for you? Well, it means the model can tackle complex tasks that require logical inference, reasoning, math, coding, and language understanding with remarkable accuracy and efficiency.

For developers eager to dive in, the DeepSeek-R1 model is now available as an nVidia NIM microservice preview on build.nvidia.com. Imagine being able to experiment with a model that can deliver up to 3,872 tokens per second on a single NVIDIA HGX H200 system. That’s some serious speed!

The Power Behind the Performance

So, what’s driving this incredible performance? DeepSeek-R1 is a large mixture-of-experts (MoE) model, and each layer boasts an astounding 256 experts. When you input a token, it’s routed to eight different experts in parallel for evaluation. This setup means that delivering real-time answers requires a robust infrastructure—think many GPUs with high compute performance, all connected with high-bandwidth and low-Latency communication.

Thanks to the NVIDIA Hopper architecture’s FP8 Transformer Engine and the 900 GB/s of NVLink bandwidth, a single server equipped with eight H200 GPUs can run the full 671-billion-parameter DeepSeek-R1 model at lightning speed. And if you’re curious about the future, the upcoming NVIDIA Blackwell architecture is set to take things up a notch, with fifth-generation Tensor Cores poised to deliver a staggering 20 Petaflops of peak FP4 compute performance.

Getting Started with DeepSeek-R1

Ready to see what all the fuss is about? Developers can now experience the DeepSeek-R1 NIM microservice at build.nvidia.com. This is your chance to explore how DeepSeek-R1 works and discover its potential for your own projects. With NVIDIA NIM, deploying DeepSeek-R1 is a breeze, ensuring you get the efficiency you need for agentic AI systems.

So, what are you waiting for? Dive into the world of DeepSeek-R1 and unlock new possibilities in AI reasoning. Your next big project could be just a few clicks away!

And remember, this is just the beginning—stay tuned for more updates on this technology.

Background Information

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About nVidia:

NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.

  

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Technology Explained

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Blackwell: Blackwell is an AI computing architecture designed to supercharge tasks like training large language models. These powerful GPUs boast features like a next-gen Transformer Engine and support for lower-precision calculations, enabling them to handle complex AI workloads significantly faster and more efficiently than before. While aimed at data centers, the innovations within Blackwell are expected to influence consumer graphics cards as well

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Latency: Technology latency is the time it takes for a computer system to respond to a request. It is an important factor in the performance of computer systems, as it affects the speed and efficiency of data processing. In the computer industry, latency is a major factor in the performance of computer networks, storage systems, and other computer systems. Low latency is essential for applications that require fast response times, such as online gaming, streaming media, and real-time data processing. High latency can cause delays in data processing, resulting in slow response times and poor performance. To reduce latency, computer systems use various techniques such as caching, load balancing, and parallel processing. By reducing latency, computer systems can provide faster response times and improved performance.

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Petaflops: Petaflops is a measure of computing speed, specifically one quadrillion floating-point operations per second. This technology is used to measure the performance of supercomputers, which are extremely powerful computers used for complex calculations and simulations. Petaflops technology has revolutionized the computer industry by allowing for faster and more efficient processing of large amounts of data. This has enabled advancements in fields such as weather forecasting, climate modeling, and drug discovery. Petaflops technology has also been utilized in artificial intelligence and machine learning, allowing for more accurate and sophisticated algorithms. In simpler terms, Petaflops is like a race car for computers, allowing them to process information at lightning-fast speeds and tackle complex problems that were previously impossible to solve.

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Tensor Cores: Tensor Cores are a type of specialized hardware designed to accelerate deep learning and AI applications. They are used in the computer industry to speed up the training of deep learning models and to enable faster inference. Tensor Cores are capable of performing matrix operations at a much faster rate than traditional CPUs, allowing for faster training and inference of deep learning models. This technology is used in a variety of applications, including image recognition, natural language processing, and autonomous driving. Tensor Cores are also used in the gaming industry to improve the performance of games and to enable more realistic graphics.

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