Boost AI Performance with AMD CDNA, ROCm, vLLM, and SGLang

Introduction

As AI applications become more demanding, optimizing performance is key to success. AMD, with its powerful CDNA architecture and ROCm software stack, is quickly becoming a top choice for high-performance AI solutions. Unlike NVIDIA’s CUDA ecosystem, AMD offers a cost-effective alternative, especially in specific workloads. With frameworks like vLLM and SGLang optimizing popular AI models, developers now have even more options to accelerate their AI workflows. This article explores how AMD’s innovations are shaping the future of AI performance.

CDNA

Imagine you’re asked to build a super-powerful computing system, one that can handle massive tasks like processing huge amounts of data or running complex machine learning models. You need a GPU that can get the job done—and that’s where AMD’s CDNA architecture comes into play. This GPU is designed to deliver top performance when it comes to floating-point operations per second (FLOPs), making it an essential tool in AI, scientific computing, and data-heavy applications. And here’s the thing—AMD didn’t just stop at one version; they’ve been improving this architecture over time, with each new version getting more powerful than the last.

It all started with the original CDNA, which had impressive performance. It was built using a 7nm FinFET process, which worked well, but it wasn’t as refined as what would come later. As the years went on, AMD rolled out CDNA 2, CDNA 3, and CDNA 4 models, each bringing major upgrades. CDNA 2 introduced a 6nm FinFET process, which improved power efficiency. But the real game-changer came with CDNA 3 and 4, which used both 5nm and 6nm FinFET processes (and even 3nm with CDNA 4), creating a much more efficient and powerful system. Each version made the GPU faster, more capable, and ready for bigger, more complex tasks.

One thing that really stands out about CDNA’s evolution is how the number of transistors has grown. The original CDNA had 25.6 billion transistors—pretty impressive, right? But CDNA 2 bumped that up to 58 billion. When CDNA 3 came along, that number shot up to 146 billion, and with CDNA 4, we’re talking a massive 185 billion transistors. This is like taking your car’s engine and adding extra horsepower—so now, it has the muscle to handle even the toughest challenges.

Of course, all that power needs to be processed efficiently, and that’s where Compute Units (CUs) and Matrix Cores come in. The original CDNA had 120 CUs and 440 Matrix Cores, which allowed it to handle multiple tasks at once. But AMD didn’t stop there. CDNA 2 pushed that to 220 CUs and 880 Matrix Cores, while CDNA 3 took it even further, with 304 CUs and 1,216 Matrix Cores. The latest version, CDNA 4, offers 256 CUs and 1,024 Matrix Cores, meaning it can handle more AI models and calculations faster and more efficiently.

Now, let’s talk memory—because in AI and high-performance computing, memory is everything. The original CDNA GPU came with 32GB of HBM2 memory, but as the architecture progressed, its memory handling got a major upgrade. CDNA 2 bumped that up to 128GB of HBM2E, a faster, more efficient type of memory. With CDNA 3, the memory got even stronger, with up to 256GB of HBM3/HHM3E. And the latest CDNA 4? It comes with a staggering 288GB of HBM3E memory, easily handling the enormous datasets needed for today’s AI models.

Here’s a big deal: the speed at which memory can be accessed is just as important as how much memory there is. The original CDNA GPU supported a peak memory bandwidth of 1.2 terabytes per second (TB/s). CDNA 2 upped that to 3.2 TB/s, and CDNA 3 pushed it to 6 TB/s. But with CDNA 4, AMD pushed the limits even further, reaching an impressive 8 TB/s—perfect for high-throughput applications in scientific computing and generative AI.

Another cool feature that came with CDNA 3 and 4 is the AMD Infinity Cache™. Earlier versions didn’t have this, but CDNA 3 and 4 added 256 MB of Infinity Cache, which helps reduce memory latency and boost memory bandwidth for tasks that demand a lot of memory. Imagine it like building an express lane on your data highway, giving your GPU faster access to the info it needs to perform its best.

CDNA’s architecture also introduced GPU coherency—something the original model didn’t have. With CDNA 2, AMD added cache coherency, which meant different parts of the GPU could share and access memory more efficiently. This was further improved in CDNA 3 and 4, where both cache and high-bandwidth memory (HBM) coherency were added, speeding up data access and overall GPU performance. This is especially useful for workloads that need frequent memory updates, like complex machine learning tasks.

When it comes to supporting various data types, CDNA has got it all covered. The original CDNA supported basic data types like INT4, INT8, BF16, FP16, FP32, and FP64. But with CDNA 2 and 3, AMD added support for more complex data types like TF32 and sparse data types, which boost performance when working with sparse matrices—a key feature for deep learning. CDNA 4 took it a step further by supporting even more data types, like INT4, FP4, FP6, INT8, FP8, BF16, FP16, TF32*, FP32, and FP64, along with added sparsity support to improve performance in AI and machine learning tasks.

So, who gets to use all this power? AMD’s CDNA architecture is part of several AMD Instinct™ product lines, such as the MI100, MI200, MI300, and MI350 series. These GPUs are designed to meet the needs of AI researchers, high-performance computing (HPC) pros, and data center operators. These aren’t just academic tools—they’re also essential in industries that are pushing the limits of AI and data analysis.

Let’s not forget about TF32 support in CDNA 4. This feature isn’t directly supported in hardware, but it’s made possible through software emulation, meaning you can still take advantage of it when working with the latest AI models.

With every new version of CDNA, AMD continues to break new ground in GPU computing. The increasing memory bandwidth, more powerful computational cores, and better handling of data ensure developers have the tools they need to take full advantage of modern machine learning models and scientific simulations. Looking ahead, it’s clear that CDNA will remain at the forefront of high-performance computing, driving innovation and advancing AI’s potential.

AMD CDNA Architecture Overview

ROCm Software Stack

Imagine you’re a developer who’s just been given the keys to a powerhouse of potential: AMD’s GPUs. These beasts are made for high-performance computing, and you know they’ve got the power to handle even the most complex tasks. But here’s the thing—how do you take all that raw power and make it work for you? That’s where ROCm, AMD’s open-source software stack, comes in. Think of it like your toolkit to get the most out of the heavy-duty hardware AMD provides. It’s full of tools, libraries, and drivers, all designed to help you unlock the full potential of AMD GPUs, whether you’re crunching numbers, running AI models, or diving into deep learning projects.

One of the coolest features of ROCm is its support for the HIP programming model—HIP stands for Heterogeneous-Compute Interface for Portability. Sounds like a lot of technical jargon, right? But honestly, it’s simpler than it sounds. HIP allows you to write code that works on both AMD and NVIDIA GPUs with minimal changes. So, if you’ve spent time working with NVIDIA’s CUDA , you’ll feel right at home. It’s like driving two different sports cars—they’re not the same, but the experience is familiar, so you can focus on what really matters, whether that’s the road or the code. The best part is that it doesn’t matter which GPU you’re using; HIP makes it easy to switch between them without having to rewrite your code from scratch.

But hold up, there’s even more! ROCm doesn’t just stop with HIP. It offers a whole range of programming options, so you can choose what works best for your needs. For example, if you like using OpenCL (Open Computing Language), that’s perfect for cross-platform development. With OpenCL, you’re not tied to one hardware vendor. You can write your code to run on any platform—AMD, NVIDIA, or other hardware you might be working with. It’s like having a universal remote that controls everything, from your TV to the music to the lights. OpenCL gives you that same kind of flexibility.

Then there’s OpenMP (Open Multi-Processing). This one’s for when you want to scale your app and dive into some serious multi-threading. OpenMP uses simple compiler directives to parallelize your code, which means you don’t have to get bogged down in the details of threading and synchronization. It’s perfect for when you’re working with huge datasets or need your computations spread across several processing units. Think of OpenMP like a manager who’s great at assigning tasks—let it handle the heavy lifting of managing parallelism, while you focus on writing the logic for your app.

Together, these programming models—HIP, OpenCL, and OpenMP—turn the ROCm software stack into a powerful, flexible environment for building and deploying high-performance applications on AMD GPUs. Whether you’re a CUDA pro who wants to stick with something familiar, a developer who loves the flexibility of OpenCL, or someone who needs to scale things up with OpenMP, ROCm’s got you covered. It’s like a Swiss army knife for developers working with AMD’s powerful hardware, giving you everything you need to tackle a wide range of high-performance computing tasks.

Inference with AMD

Imagine you’re running a company that needs to roll out complex AI models at lightning speed. You’ve got all these advanced algorithms, and you need a way to make sure they run smoothly—no hiccups or delays. That’s when AMD steps in, teaming up with some of the best frameworks out there, like vLLM and SGLang , to create highly optimized containers for inference tasks.

But these aren’t just any containers. They’re specifically built to handle large-scale deployments of generative AI models, making the whole process much smoother and faster. These containers come with a game-changing feature: Day 0 support. This means that AMD’s solutions work with the latest and most popular generative AI models right from the very first day they’re released. For businesses and developers who need to stay up-to-date with the latest tech, this is a huge win—you can deploy those models without missing a beat.

Speaking of vLLM , this tool is a real gem for general-purpose inference tasks. Not only is it flexible, but it’s also super easy to use, making it perfect for developers working with a range of AI models. Whether you’re dealing with text generation, image processing, or something else entirely, vLLM has got your back.

AMD doesn’t just leave you with a solid platform either—they also offer continuous support with bi-weekly stable releases and weekly updates. This means vLLM is always improving, with new features and tweaks to make sure it’s ready for anything you throw at it. If you’re in the AI space, vLLM is one of those tools that just keeps delivering time after time.

But maybe you’re not just after something versatile. Maybe you need something more specific, like agentic workloads or niche applications. That’s where SGLang comes in. Tailored for specific AI use cases, SGLang is the go-to framework for those of you working on tasks that require a more targeted approach. AMD ensures that SGLang is always up-to-date with weekly stable releases, so you don’t have to worry about compatibility or system stability when deploying your applications. With all this support, you can dive into your work confidently, knowing that your setup is always ready to go.

And of course, it doesn’t stop with just the frameworks. AMD is fully committed to optimizing the most widely used AI models to ensure they run seamlessly on their hardware. You’ve probably heard of models like the Llama family, Gemma 3 , Deepseek , or the Qwen family—they’re all part of AMD’s focus on making sure the best AI models work well on their platform. Thanks to Day 0 support, these models are always ready to work with the latest hardware, meaning you won’t fall behind in today’s rapidly evolving AI world.

This proactive approach is essential when dealing with AI models that are always evolving. AMD’s forward-thinking solutions make sure your AI applications stay ahead of the curve, allowing you to deploy the latest tech with full confidence. With all these tools and frameworks, AMD doesn’t just create powerful hardware—it ensures that developers have everything they need to get the most out of their AI models. Whether you’re working with vLLM , SGLang , or any of the latest AI models, AMD has you covered, providing the kind of seamless integration you need to build and deploy AI solutions at scale.

AMD AI Solutions

Conclusion

In conclusion, AMD’s CDNA architecture and ROCm software stack are revolutionizing AI performance, offering developers a cost-effective alternative to NVIDIA’s dominant CUDA ecosystem. With the added power of frameworks like vLLM and SGLang, AMD provides a comprehensive solution that optimizes AI model deployment and processing. As the demand for high-performance AI applications continues to grow, AMD’s hardware is well-positioned to compete in the evolving AI accelerator market, giving developers more flexibility and options. Looking ahead, we can expect further innovations and refinements in AMD’s offerings, which will continue to shape the future of AI acceleration.Snippet for Search Results: AMD’s CDNA architecture and ROCm software stack are transforming AI performance, providing cost-effective alternatives and powerful tools for developers.

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