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    skd1

  • AI Hardware Ecosystem

    Comparison Chart: GPUs and TPUs

    NVIDIA GeForce RTX 4090NVIDIARTX 4090~16,384 CUDA CoresHigh-end gaming and AI GPU; great for heavy modelsConsumer market
    AMD Radeon RX 7900 XTXAMDRX 7900 XTX~6,144 Stream ProcessorsHigh-end Radeon GPU; value for performanceConsumer market
    Intel Arc GPUIntelVarious Arc ModelsVariesIntel’s entry into the GPU space; more budget-orientedConsumer market
    Google TPU v5GoogleTPU v5Not publicly listed in coresOptimized specifically for AI/ML tasks; cloud-basedGoogle Cloud only

    Nvidia – RTX Generation comparison chart

    https://www.nvidia.com/en-us/geforce/graphics-cards/compare

    NVIDIA RTX 4090: Very powerful, lots of CUDA cores, great for gaming and AI.

    AMD Radeon RX 7900 XTX: High performance with slightly fewer cores but good value.

    Intel Arc: A newer, budget-friendly entry into the GPU market.

    Google TPU v5: Not for direct purchase, cloud-only, specialized for machine learning workloads.

    Understanding Stream Processors, CUDA Cores, and TPUs

    CUDA Cores (NVIDIA):

    • CUDA cores are essentially the parallel processing units inside NVIDIA GPUs. Think of them like tiny workers that handle multiple tasks at once, especially useful for graphics rendering and parallel computations in AI workloads.

    Stream Processors (AMD):

    • Stream processors are AMD’s equivalent to NVIDIA’s CUDA cores. They do a similar job—handling parallel tasks to process graphics and compute workloads. While the architecture differs,

    CUDA Kernel

    Nvidia GPU

    CUDA Toolkit 12.4 download

    • device drivers
    • runtime
    • Devtools
    • compiler

    wrote in C++

    CPU communicate with CUDA and ask to run CUDA Kernel.