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@article{206329,
        author = {Benjamin Davies},
        title = {A Comparative Analysis of Gaming and Data Centre GPUs: Evaluating the NVIDIA RTX 4090 and A100 Across AI, HPC, and Graphics Workloads},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2024-2031},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206329},
        abstract = {This study presents a comparative analysis of two NVIDIA Graphics Processing Units (GPUs) designed for fundamentally different applications: the A100, a data centre GPU optimized for artificial intelligence training and high-performance computing, and the RTX 4090, a consumer GPU designed for gaming and graphics rendering. Using secondary sources including manufacturer specifications, benchmark data, and industry analyses, this paper compares the architecture, key features, and performance of both GPUs across AI training, 4K gaming, 3D rendering, and floating-point precision workloads. Findings indicate that the A100 outperforms the RTX 4090 by approximately 3x in AI training due to its superior VRAM capacity, NVLink interconnect, and multi-instance partitioning capabilities, while the RTX 4090 achieves approximately 10x higher 4K gaming performance through dedicated ray tracing cores and DLSS 3 frame generation. The study concludes that each GPU excels within its target domain, and that neither can effectively substitute for the other across all workloads.},
        keywords = {Graphics Processing Unit, NVIDIA A100, NVIDIA RTX 4090, GPU benchmarking, artificial intelligence training, high-performance computing, Ada Lovelace architecture},
        month = {July},
        }