AMD and Intel agreed on something pretty unusual. A local PyTorch AI app shouldn't have to care whether the CPU underneath it is AMD or Intel. AMD is folding its ZenDNN AI optimizations into oneDNN, the library originally developed by Intel. AMD + Intel have now aligned on oneDNN as the default open-source x86 acceleration path in PyTorch. ZenDNN has been working on refining for ... 🧠 LLM inference ⚡ INT4 / INT8 / BF16 / FP16 🧩 fused MoE operations 🚀 vLLM CPU acceleration 🔧 optimized attention/matmul Now that work is being brought into the common oneDNN ecosystem AMD + Intel are also jointly developing ACE, new x86 AI matrix instructions. The plan is for future AMD/Intel AI instructions → oneDNN → PyTorch So local PyTorch software gets one x86 target instead of separate vendor paths. ⚠️ This does NOT make llama.cpp/GGUF faster, and AMD/Intel haven't published new performance numbers from the merger. 🎯But for CPU Local AI, the two companies that spent decades fighting over x86 are now cooperating on the AI software layer. Link in ALT