Is AMD Actually Competing with NVIDIA in local AI? The Real Story

Summary

This video explores AMD's efforts to compete with Nvidia in AI GPU performance, highlighting both hardware and software challenges. It examines AMD’s ROCm platform, HIP tools, and alternatives like Vulkan, contrasting them with Nvidia’s mature CUDA ecosystem. While AMD GPUs perform well under Linux with ROCm, Windows support is limited and often relies on suboptimal workarounds such as DirectML or ZLUDA. The video also discusses AMD’s proprietary Amuse tool, GPU compatibility issues, and performance comparisons, showing that Nvidia generally outperforms AMD for AI workloads at similar price points. Practical tips for using AMD GPUs for AI locally are shared, emphasizing Linux as the better platform.

Takeaways

• 💻 Nvidia's CUDA, launched in 2007, became the standard for GPU-based AI and scientific computing due to early adoption and broad developer support.
• 🔧 AMD's ROCm, released in 2016, arrived much later and initially only supported Linux, limiting adoption compared to CUDA.
• 🛠️ ROCm HIP allows developers to port CUDA code to AMD GPUs, but it requires active code modification and is not a plug-and-play solution.
• 🪟 Windows support for ROCm is limited; HIP does not provide full ROCm functionality, and alternative tools like Vulkan and Amuse have significant limitations.
• 🐧 Linux users benefit more from AMD GPUs for AI workloads because ROCm is fully supported and delivers near-competitive performance with Nvidia GPUs.
• ⚡ PyTorch simplifies AI development by abstracting GPU backends, but AMD support is inconsistent: ROCm works on Linux, while Windows relies on DirectML with major performance drawbacks.
• 🖼️ AI image generation performance on AMD GPUs is generally weaker than Nvidia GPUs when using less-optimal Windows solutions like DirectML, but ROCm on Linux improves competitiveness.
• ⏳ Nvidia provides longer support cycles for older GPUs, whereas AMD often drops support earlier, affecting both gaming and AI workloads.
• 🔒 Legal uncertainties around tools like ZLUDA complicate running CUDA applications on AMD Windows systems, leaving developers reliant on workarounds.
• 🚀 AMD's focus on proprietary tools like Amuse instead of expanding ROCm for Windows reduces flexibility for developers compared to open-source alternatives.
• 💡 For optimal AI performance on AMD GPUs, Linux remains the preferred environment, while Windows users face limitations and must rely on WSL2 or specialized workarounds.

Q & A

What is the main difference between Nvidia's CUDA and AMD's ROCm platforms?

CUDA, released in 2007, quickly became the standard for AI and scientific computing by allowing GPUs to handle general-purpose tasks. ROCm, AMD's equivalent released in 2016, arrived later, initially supported only Linux, and lacks full Windows compatibility, making it less widespread among developers.

What is ROCm HIP and how does it relate to Windows support?

ROCm HIP, announced for Windows in 2023, is a tool that helps developers port CUDA code to AMD GPUs. However, it is not a full implementation of ROCm AI libraries on Windows and requires developers to modify their code manually.

Why is software support more important than hardware for AI workloads?

AI performance heavily depends on optimized software frameworks and libraries like PyTorch, CUDA, and ROCm. Even powerful GPUs can underperform if the software ecosystem is limited, as seen with AMD GPUs on Windows using DirectML.

What alternatives exist for running AI workloads on AMD GPUs under Windows?

Alternatives include Vulkan (a cross-platform graphics and compute API), ZLUDA (uses ROCm HIP to run CUDA applications), and WSL2 ROCm, though each has limitations such as compatibility, performance, or GPU restrictions.

How does PyTorch support differ between Nvidia and AMD GPUs?

PyTorch seamlessly supports CUDA on Windows and Linux for Nvidia GPUs. For AMD, PyTorch works well with ROCm on Linux, but on Windows, developers must use DirectML, which has severe memory issues, performance penalties, and limited functionality.

How does AMD's Amuse tool compare to open-source AI tools?

Amuse is a proprietary image generation tool that supports selected models in the ONNX format. It lacks the flexibility and feature set of open-source tools like ComfyUI or Automatic1111, which allow more customization and broader model support.

What limitations exist for AMD ROCm support on Windows through WSL2?

ROCm via WSL2 only supports RX 7000 series GPUs and above. Older GPUs, such as the RX 6800, may not work or install, limiting accessibility for users with previous-generation hardware.

How does Nvidia's support for older GPUs compare to AMD's?

Nvidia typically offers longer support cycles, with older GPUs continuing to receive updates for CUDA and AI workloads. AMD tends to drop support earlier, affecting both gaming and AI performance for older hardware.

How do AMD and Nvidia GPUs compare in AI performance for image generation?

Using optimal setups like ROCm on Linux, AMD GPUs like the RX6800 perform slightly worse than Nvidia GPUs such as the RTX 3060. When suboptimal setups like DirectML on Windows are used, AMD GPUs perform significantly worse than Nvidia GPUs using CUDA.

What is the overall conclusion regarding AMD's competitiveness in AI GPU computing?

AMD provides viable AI performance on Linux, but software limitations, Windows support gaps, and shorter hardware support cycles make it less competitive than Nvidia. Nvidia remains the preferred choice for most AI workloads, especially on Windows.

What is the significance of Vulkan in running AI models on AMD GPUs?

Vulkan serves as a cross-platform API for high-performance compute tasks and is used as an alternative to DirectX or OpenGL for AI. Some models, like GPT-based LLMs, can run on Vulkan, but bugs in AMD's implementation have limited performance for certain versions.

Why might AMD users consider Linux over Windows for AI workloads?

Linux offers native ROCm support, better performance, and compatibility with tools like ComfyUI and PyTorch, while Windows support is fragmented, slower, or limited to workarounds like DirectML or WSL2 ROCm with hardware restrictions.