AMD Instinct GPUs Gear Up for Challenging Today’s Advanced AI Workloads

Navigating the AI Landscape: AMD’s Approach to Infrastructure

When it comes to choosing the right AI infrastructure, customers today are on a quest for the best of both worlds: industry-standard benchmarks and real-world performance metrics. Think about it—who wouldn’t want to make informed decisions based on solid data? With heavyweights like Llama 3.1 405B and DeepSeek-R1 in the mix, the stakes are high. At AMD, we’re not just watching from the sidelines; we’re diving in headfirst. We believe that delivering value across these dimensions is crucial for wider AI adoption and real-world deployment at scale.

So, how do we tackle this? By taking a holistic approach, of course! We optimize performance for rigorous benchmarks like MLPerf while also offering Day 0 support and rapid tuning for the models that our customers actually use in production. This means that our AMD Instinct GPUs don’t just perform well in standardized tests; they also excel in real-world applications, making AI inferencing both high-throughput and scalable. Stick around as we explore how our commitment to benchmarking, open model enablement, and software tools unlocks greater value for our customers.

A Milestone Moment: AMD’s Firsts in MLPerf Inference 5.0

In the recent MLPerf Inference 5.0 round, AMD made waves with a series of impressive firsts that showcase our growing momentum in this critical industry benchmark. For the first time ever, we submitted MLPerf inference numbers for our latest AMD Instinct MI325X GPU, launched just last October. But that’s not all—we also supported the first-ever multi-node submission using our Instinct solution in collaboration with a partner. And guess what? We enabled multiple partners to submit results using our latest GPUs for the very first time. Talk about a game changer!

Expanding Our Reach: Industry Adoption on the Rise

We’re thrilled to report that several partners, including SMC, ASUS, and Gigabyte (GCT) with the Instinct MI325X, along with MangoBoost using the Instinct MI300X, have successfully submitted MLPerf results for the first time. The performance of these partner submissions on Llama 2 70B was on par with AMD’s own results (check out Figure 1), highlighting the consistency and reliability of our GPUs across various environments.

But we didn’t stop there! AMD has also extended its submissions to include Stable Diffusion XL (SDXL) with the latest Instinct MI325X GPUs. Our unique GPU partitioning techniques played a crucial role in achieving competitive performance against nVidia’s H200 in our inaugural SDXL submission.

Pushing Boundaries: A Record-Breaking Multi-Node Submission

Let’s talk about MangoBoost for a moment. This innovative company, dedicated to maximizing AI data center efficiency, made history with the first-ever partner submission to MLPerf using multiple nodes of AMD Instinct solutions—specifically, four nodes of the Instinct MI300X. This submission set a new benchmark, achieving the highest offline performance ever recorded in MLPerf submissions for the Llama 2 70B benchmark (see Figure 2). This achievement speaks volumes about the scalability and performance of AMD Instinct solutions in multi-node AI workloads.

Unlocking Performance: Insights from MLPerf

The secret sauce behind AMD’s strong MLPerf Inference 5.0 results? It’s all about the synergy between our hardware and software innovations. Each MI325X node packs a punch with 2.048 TB of HBM3E memory and 6 TB/s bandwidth, allowing models like Llama 2 70B and SDXL to run entirely in memory on a single GPU. This means no cross-GPU overhead and maximum throughput!

Our bi-weekly ROCm containers, available via Infinity Hub, brought critical optimizations in kernel scheduling, GEMM tuning, and inference efficiency. Plus, the AMD Quark tool enables FP16-to-FP8 quantization, while enhancements in vLLM and memory handling further boost inference performance. With the new AI Tensor Engine for ROCm (AITER), we’re accelerating operations like GEMM and Attention—delivering up to 17× faster decoder execution and over 2× throughput in LLM inference. Curious about AITER? You can read more about it here.

Building on Success: Performance with Open-Source Models

Riding high on our MLPerf achievements, AMD continues to deliver exceptional performance on leading open-source AI models, particularly DeepSeek-R1 and Llama 3.1 405B. Optimized for AMD Instinct MI300X GPUs, DeepSeek-R1 has seen a stunning 4X inference speed boost in just 14 days thanks to rapid ROCm optimizations. Competing directly with NVIDIA’s H100, the MI300X holds its own against the H200 (see Figure 3), making it an excellent choice for scalability and efficiency.

The Llama 3.1 405B model is another feather in our cap, optimized for AMD Instinct MI300X GPUs. With its superior performance in memory-bound workloads, the MI300X outshines the H100, and it even cuts down on infrastructure costs by requiring fewer nodes for large models. With Day 0 support, we’ve ensured a seamless deployment and optimization experience for this model. Want to know how to reproduce this benchmark? Check it out here.

Looking Ahead: AMD’s Commitment to Transparency and Innovation

AMD’s dedication to AI scalability, performance, and open-source strategies shines through in our MLPerf v5.0 results and industry collaborations. With the MI300X and MI325X, we’re delivering high-performance AI solutions that drive efficiency and cost-effectiveness.

As we continue to push the boundaries of AI, AMD remains committed to transparency and empowering our customers to scale AI confidently. Keep an eye out for our next MLPerf submission—we can’t wait to share our progress and insights with you!

And don’t forget, all results can be reproduced by following the instructions in our ROCm blog post. For a deeper dive into the MLPerf optimizations we made this round, check out our detailed blog. Full submission results are available on the MLCommons website, and source artifacts can be found in this repository.

Background Information

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About AMD:

AMD, a large player in the semiconductor industry is known for its powerful processors and graphic solutions, AMD has consistently pushed the boundaries of performance, efficiency, and user experience. With a customer-centric approach, the company has cultivated a reputation for delivering high-performance solutions that cater to the needs of gamers, professionals, and general users. AMD's Ryzen series of processors have redefined the landscape of desktop and laptop computing, offering impressive multi-core performance and competitive pricing that has challenged the dominance of its competitors. Complementing its processor expertise, AMD's Radeon graphics cards have also earned accolades for their efficiency and exceptional graphical capabilities, making them a favored choice among gamers and content creators. The company's commitment to innovation and technology continues to shape the client computing landscape, providing users with powerful tools to fuel their digital endeavors.

  

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About ASUS:

ASUS, founded in 1989 by Ted Hsu, M.T. Liao, Wayne Hsieh, and T.H. Tung, has become a multinational tech giant known for its diverse hardware products. Spanning laptops, motherboards, graphics cards, and more, ASUS has gained recognition for its innovation and commitment to high-performance computing solutions. The company has a significant presence in gaming technology, producing popular products that cater to enthusiasts and professionals alike. With a focus on delivering and reliable technology, ASUS maintains its position as a important player in the industry.

  

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About Gigabyte:

Gigabyte Technology, a important player in the computer hardware industry, has established itself as a leading provider of innovative solutions and products catering to the ever-evolving needs of modern computing. With a strong emphasis on quality, performance, and technology, Gigabyte has gained recognition for its wide array of computer products. These encompass motherboards, graphics cards, laptops, desktop PCs, monitors, and other components that are integral to building high-performance systems. for their reliability and advanced features, Gigabyte's motherboards and graphics cards have become staples in the gaming and enthusiast communities, delivering the power and capabilities required for immersive gaming experiences and resource-intensive applications

  

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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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GPU: GPU stands for Graphics Processing Unit and is a specialized type of processor designed to handle graphics-intensive tasks. It is used in the computer industry to render images, videos, and 3D graphics. GPUs are used in gaming consoles, PCs, and mobile devices to provide a smooth and immersive gaming experience. They are also used in the medical field to create 3D models of organs and tissues, and in the automotive industry to create virtual prototypes of cars. GPUs are also used in the field of artificial intelligence to process large amounts of data and create complex models. GPUs are becoming increasingly important in the computer industry as they are able to process large amounts of data quickly and efficiently.

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HBM3E: HBM3E is the latest generation of high-bandwidth memory (HBM), a type of DRAM that is designed for artificial intelligence (AI) applications. HBM3E offers faster data transfer rates, higher density, and lower power consumption than previous HBM versions. HBM3E is developed by SK Hynix, a South Korean chipmaker, and is expected to enter mass production in 2024. HBM3E can achieve a speed of 1.15 TB/s and a capacity of 64 GB per stack. HBM3E is suitable for AI systems that require large amounts of data processing, such as deep learning, machine learning, and computer vision.

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LLM: A Large Language Model (LLM) is a highly advanced artificial intelligence system, often based on complex architectures like GPT-3.5, designed to comprehend and produce human-like text on a massive scale. LLMs possess exceptional capabilities in various natural language understanding and generation tasks, including answering questions, generating creative content, and delivering context-aware responses to textual inputs. These models undergo extensive training on vast datasets to grasp the nuances of language, making them invaluable tools for applications like chatbots, content generation, and language translation.

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Stable Diffusion: Stable Diffusion is a technology that is used to improve the performance of computer systems. It is a process of spreading out the load of a system across multiple processors or cores. This helps to reduce the amount of time it takes for a system to complete a task, as well as reduce the amount of energy used. Stable Diffusion is used in many areas of the computer industry, such as in cloud computing, distributed computing, and high-performance computing. It is also used in gaming, where it can help to reduce the amount of time it takes for a game to load. Stable Diffusion is also used in artificial intelligence, where it can help to improve the accuracy of machine learning algorithms.

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