The performance comparison between the NVIDIA GeForce RTX 4090 and the newly launched RTX 5090 in AI tasks reveals significant advancements in the latter, primarily due to architectural improvements and enhanced core capabilities.
Performance Metrics
1. Tensor Cores: The RTX 5090 features 680 Tensor Cores, a notable increase from the 512 found in the RTX 4090. This upgrade allows the RTX 5090 to achieve up to 3,352 trillion operations per second (TOPS) for AI workloads, making it approximately 2.5 times faster than the RTX 4090, which processes around 1,321 TOPS [1][2].
2. CUDA Cores and Processing Power: The RTX 5090 has 21,760 CUDA cores, compared to the 16,384 CUDA cores in the RTX 4090. This increase contributes to a substantial rise in computational capability, with the RTX 5090 delivering up to 125 TFLOPS versus the 83 TFLOPS of the RTX 4090 [1][2].
3. Memory Bandwidth and Capacity: The memory architecture has also been upgraded; the RTX 5090 comes with 32GB of GDDR7 memory on a 512-bit memory bus, resulting in a bandwidth of 1.79 TB/s, nearly double that of the RTX 4090's 1.01 TB/s with its 24GB of GDDR6X memory. This increased bandwidth is particularly beneficial for AI tasks that require rapid access to large datasets [1][4].
AI-Specific Enhancements
- The introduction of DLSS 4, which includes Multi Frame Generation, allows the RTX 5090 to generate up to three additional frames per rendered frame. This not only enhances gaming performance but also significantly improves visual fidelity and responsiveness in AI-driven applications [1][2].
- The new architecture supports advanced features like Ray Reconstruction and Super Resolution, which are optimized for AI tasks, further enhancing both gaming and productivity applications [1][2].
Conclusion
Overall, the RTX 5090 represents a substantial leap forward in performance for AI tasks compared to the RTX 4090. With its superior Tensor Core capabilities, increased CUDA core count, enhanced memory bandwidth, and advanced AI features, it is positioned as a powerhouse for both gaming and professional AI workloads, effectively redefining what users can expect from high-performance GPUs in this domain [1][2][3].
Citations:
[1] https://www.storagereview.com/review/nvidia-geforce-rtx-5090-review-pushing-boundaries-with-ai-acceleration
[2] https://beebom.com/nvidia-rtx-5090-vs-rtx-4090-comparison/
[3] https://www.windowscentral.com/hardware/rtx-5090-should-you-buy
[4] https://techloot.co.uk/gadgets/nvidia-rtx-5090-vs-rtx-4090-ultimate-gpu-showdown/
[5] https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
[6] https://www.reddit.com/r/nvidia/comments/1hvv7u3/ive_created_a_chart_comparing_the_native_4k_path/
[7] https://pk.ign.com/nvidia-rtx-5090/234959/review/nvidia-geforce-rtx-5090-founders-edition-review
[8] https://www.tomshardware.com/pc-components/gpus/nvidia-geforce-rtx-5090-versus-rtx-4090-how-does-the-new-halo-gpu-compare-with-its-predecessor
[9] https://www.nvidia.com/en-us/geforce/news/geforce-rtx-5090-5080-dlss-4-game-ready-driver/