AI Hardware Ecosystem

Comparison Chart: GPUs and TPUs


NVIDIA GeForce RTX 4090	NVIDIA	RTX 4090	~16,384 CUDA Cores	High-end gaming and AI GPU; great for heavy models	Consumer market
AMD Radeon RX 7900 XTX	AMD	RX 7900 XTX	~6,144 Stream Processors	High-end Radeon GPU; value for performance	Consumer market
Intel Arc GPU	Intel	Various Arc Models	Varies	Intel’s entry into the GPU space; more budget-oriented	Consumer market
Google TPU v5	Google	TPU v5	Not publicly listed in cores	Optimized specifically for AI/ML tasks; cloud-based	Google Cloud only

Nvidia – RTX Generation comparison chart

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.

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

wrote in C++

CPU communicate with CUDA and ask to run CUDA Kernel.