CUDA, developed by NVIDIA, is a proprietary platform designed specifically for NVIDIA GPUs, meaning it doesn’t natively run on hardware from other manufacturers like AMD or Intel. However, there are alternatives that allow CUDA-like functionality or CUDA code to be used on non-NVIDIA GPUs through various compatibility layers, frameworks, or competing technologies. Here's a breakdown of CUDA-compatible alternatives or substitutes for non-NVIDIA GPUs:
AMD GPUs
AMD doesn’t support CUDA natively, but there are ways to adapt CUDA code or achieve similar parallel computing capabilities:

    ROCm (Radeon Open Compute)  
        AMD’s open-source platform for GPU computing, designed as a direct competitor to CUDA.  
        It supports high-performance computing (HPC) and machine learning workloads on AMD GPUs, like the Radeon Instinct series (e.g., MI300X) or high-end Radeon consumer cards (e.g., RX 7900 XTX).  
        While ROCm doesn’t run CUDA code directly, tools like HIP (Heterogeneous-compute Interface for Portability) bridge the gap. HIP allows developers to write code that compiles for both AMD (via ROCm) and NVIDIA (via CUDA) GPUs, with minimal changes to CUDA source code. The HIPify tool can automate much of this conversion.  
        Pros: Native AMD solution, growing ecosystem, supports popular frameworks like TensorFlow and PyTorch (with ROCm ports).  
        Cons: Limited hardware support (mainly newer Instinct and select Radeon GPUs), less mature than CUDA.
    ZLUDA  
        An open-source project that acts as a drop-in replacement for CUDA, enabling CUDA applications to run on AMD GPUs without code modification.  
        Originally funded by AMD, it’s now maintained independently and has shown promising results (e.g., running Blender or PyTorch on a Radeon 7900 XTX).  
        Pros: Minimal effort to use existing CUDA apps, impressive performance in some cases.  
        Cons: Not fully polished, abandoned by its primary developer for broad updates, legally murky for commercial use due to NVIDIA’s terms of service banning CUDA emulation on rival hardware.
    OpenCL (Open Computing Language)  
        A cross-platform, open-source framework supported by AMD, Intel, and even NVIDIA GPUs.  
        It’s not CUDA-compatible out of the box, but it provides similar parallel computing capabilities and can be an alternative for writing GPU-accelerated code on AMD hardware.  
        Pros: Broad compatibility across vendors, royalty-free.  
        Cons: More complex to use than CUDA, less optimized libraries, and weaker ecosystem compared to CUDA or ROCm.

Intel GPUs
Intel’s GPU offerings, like the Arc series or Data Center GPU Max (e.g., Ponte Vecchio), don’t support CUDA natively, but alternatives exist:

    oneAPI  
        Intel’s open, unified programming model for CPUs, GPUs, and other accelerators, positioned as a CUDA alternative.  
        It uses SYCL, a modern C++-based standard from the Khronos Group, to write portable, high-performance code. The DPC++ Compatibility Tool (part of oneAPI) can translate CUDA code to SYCL, enabling it to run on Intel GPUs.  
        Pros: Open standard, supports Intel’s full hardware stack, growing adoption in HPC.  
        Cons: Translation from CUDA isn’t always seamless, ecosystem still developing compared to CUDA.
    OpenCL  
        As with AMD, Intel GPUs support OpenCL for parallel computing.  
        It’s a viable option for writing GPU code without relying on CUDA, though it lacks the extensive libraries and tooling CUDA provides.  
        Pros: Works across Intel, AMD, and NVIDIA hardware.  
        Cons: Less developer-friendly, not a direct CUDA replacement.

Other Options

    Vulkan Compute: A cross-platform API from the Khronos Group, primarily for graphics but increasingly used for compute tasks. It’s not a CUDA replacement directly but can be used on AMD, Intel, and NVIDIA GPUs for general-purpose GPU (GPGPU) workloads. It requires significant code rewriting, though.
    DirectCompute: Microsoft’s compute shader framework, part of DirectX, works on Windows across multiple GPU vendors but is less common for HPC or machine learning compared to CUDA.

Key Considerations

    CUDA Code Reuse: If you have existing CUDA code, HIP (for AMD) or SYCL (for Intel) with their respective conversion tools (HIPify, DPC++ Compatibility Tool) are the most practical paths. ZLUDA is a niche but intriguing option for AMD if you want to avoid rewriting entirely.
    Performance: Alternatives like ROCm and oneAPI aim for parity with CUDA, but NVIDIA’s ecosystem (e.g., cuBLAS, cuDNN) often gives it an edge in optimized workloads like deep learning. ZLUDA has shown competitive performance in specific cases, but it’s not universally reliable yet.
    Hardware: AMD’s MI300X and Intel’s Gaudi 3 (an AI-focused accelerator) are emerging as cost-effective competitors to NVIDIA’s H100, especially as demand for cheaper AI hardware grows.

In summary, there’s no direct “CUDA-compatible” alternative that runs natively on non-NVIDIA GPUs without adaptation, since CUDA is NVIDIA-exclusive. However, HIP with ROCm (AMD), ZLUDA (AMD), and oneAPI with SYCL (Intel) provide the closest approximations, either by translating CUDA code or offering comparable parallel computing frameworks. Each has trade-offs in maturity, ease of use, and performance, so the best choice depends on your specific hardware and workload needs.