.png&w=828&q=75)
The biggest bottleneck to adopting AMD hardware is the millions of lines of legacy AI code locked into CUDA. ROCmPort AI solves this by completely automating the migration process. When a developer inputs a GitHub repository, our system initiates a 3-agent CrewAI pipeline powered by Qwen. The "CUDA Auditor" scans the AST for hardware-specific blocking code. The "ROCm Engineer" drafts a unified diff patch substituting PyTorch device logic. Finally, the "Report Writer" packages the diff alongside a generated Dockerfile and Runbook. To prove the efficacy of our pipeline, we deployed the generated patch onto the AMD Developer Cloud. Running native ROCm on an AMD Instinct MI300X, the Qwen model achieved 67.7 tokens/sec throughput with incredibly low latency. ROCmPort AI proves that with open-source agents and AMD infrastructure, the switching cost to ROCm drops to zero. (Note: The shared Lablab Hugging Face org maxed out its free CPU quotas during submission, so we have provided our identical personal Space link in the URL section for an uninterrupted live demo!)
10 May 2026