Claude Code, the famous agentic coding platform, has managed to port NVIDIA's CUDA code into the ROCm platform in just half an hour, potentially bridging the gap between the two ecosystems.
Using Claude Code For Porting From CUDA to ROCm Might Be Fine For Simpler Kernels, But Not For Complex Translations
Well, agentic workloads are indeed the next primary application of AI, and with the introduction of the likes of Claude Code and Google's Antigravity, the coding community has been disrupted by seeing the capabilities of these platforms. However, it appears that a Redditor has actually managed to bridge the gap between CUDA and ROCm using Claude Code, and according to johnnytshi, he ported an entire CUDA backend to AMD's ROCm using AI in just 30 minutes, without any translation layer in between.
Well, there are a lot of intricacies to discuss, including whether porting code with Claude is a viable option, but according to the user, the only problem they faced was with "data layout" differences. For those unaware, Claude Code operates within an agentic framework, meaning it acts intelligently to replace CUDA keywords with ROCm, ensuring the underlying logic of specific kernels remains consistent rather than simply replacing code keywords. Another advantage, of course, is that you won't need to set up complex translation environments such as Hipify; instead, you can use your CLI directly for the porting job.
However, the Redditor didn't specify what type of codebase he was working on, since ROCm essentially mimics several aspects of NVIDIA's CUDA platform; hence, a simple port won't be complex for AI. Things would become interesting once you have interconnected codebases, which would require extensive context for an agentic system to port to ROCm effectively. More importantly, since writing kernels is all about ensuring "deep hardware" optimizations, it is argued that Claude Code would still fall short in this regard, especially for specific cache hierarchies.
Efforts to break the CUDA 'moat' have been underway for several months now, with projects like ZLUDA and internal efforts by the likes of Microsoft, but NVIDIA still remains the dominant entity when it comes to writing kernels for GPU-accelerated performance.
About the author: Muhammad Zuhair is a hardware and technology reporter for Wccftech, specializing in the semiconductor industry and the complex interplay between technology, manufacturing, and geopolitics. His coverage focuses on the corporate strategies and technological roadmaps of industry giants like TSMC, NVIDIA, Samsung, and Intel. Zuhair's expertise lies in deconstructing complex topics such as fabrication nodes (e.g., 2nm process), the economic impact of policies like the CHIPS Act, and the strategic development of AI infrastructure from NVIDIA, AMD and Intel.
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