ROCm Forge: 9-Agent CUDA to AMD Migration Engine

The main hurdle to the widespread adoption of AMD GPUs (such as the MI300X) is not the hardware itself, but the huge ecosystem of AI workloads that are hardcoded to NVIDIA’s CUDA. Moving these codebases to AMD’s ROCm is notoriously difficult. Simple python scripts may just need a regex find and replace. Enterprise grade AI infrastructure calls for deep architectural translation. Introducing ROCm Forge, Team Cipher’s compiler-level, 9-Agent AI Copilot that automates the most difficult parts of CUDA-to-ROCm migration. Unlike the simple hipify scripts or black-box LLMs that hallucinate code, ROCm Forge is based on a deterministic multi-agent architecture: Hardware-Aware Scanner: Identifies implicit hardware assumptions that may lead to silent mathematical failures (e.g., hardcoded Warp Size 32 vs. AMD's 64-wide wavefront). Build Error Copilot: Scans code proactively against a run book of common ROCm build errors, suggesting required libraries automatically before compilation fails. AST Refactorer: Safely maps CUDA APIs, Dockerfiles and dependencies to their ROCm 6.2 equivalents, rating each change as Safe, Review or Manual. Health Monitor: Quantifies drift as an AMD Readiness Score and visualises in a Risk Heatmap. Deployer Agent : Generates deployment-ready artefacts optimised for the AMD Developer Cloud. We even take on the “Final Boss”: translating low-level C++ Tensor Core kernels (NVIDIA WMMA intrinsics) directly to AMD Matrix Cores. An integrated AI explainer (Llama 3.1 via Groq) delivers human-readable insights into every transformation, and developers trust the process. ROCm Forge uses compiler-level analysis and explainable AI to reduce migration effort by an estimated 65%. It doesn’t just replace strings. It saves engineers days of debugging hardware-level compiler errors, dramatically accelerating the adoption of the AMD AI ecosystem.