PNY Quadro RTX 8000 Review | 118 Data compared

The ServeTheHome review of dual NVIDIA Quadro RTX 8000 GPUs highlights the extreme capabilities of pairing these cards with an NVLink bridge to achieve 96GB of total ECC VRAM, making it a powerful solution for massive datasets and deep learning. A key benefit is the unparalleled capacity to handle professional workloads like OctaneRender, though this comes with high power requirements and a substantial cost of $5,500 per card. While excelling in professional tasks, the setup face drawbacks regarding software scaling, as many standard benchmarks fail to fully utilize the dual-card configuration. The review notes that the lack of a protective backplate and lower relative performance in certain renders, such as Redshift compared to Titan RTX, are minor limitations, though the massive VRAM capacity offers a high ROI for specific enterprise applications.

The provided review evaluates the NVIDIA Quadro RTX 8000 against the NVIDIA Tesla V100 for AI/ML deep learning and 3D rendering workloads. In synthetic and deep learning training benchmarks like Resnet50 and Inception V3 using FP16 precision, the Tesla V100 consistently outperforms the RTX 8000, achieving higher throughput such as 706.07 frames per second compared to the RTX 8000's 571.49 frames per second on standard Resnet50 FP16. However, the RTX 8000 shows an advantage in specific raw FP32 tasks and significantly beats the V100 in Blender 3D rendering, scoring 3405.61 against the V100's 2075.51. Spec-wise, the Volta-based V100 features a massive 4096-bit memory bus with 897 GB/s of HBM2 bandwidth and superior FP64 double-precision performance (7066 GFLOPS vs. 509.8 GFLOPS), while the Turing-based RTX 8000 counters with a much larger 48 GB GDDR6 frame buffer and dedicated RT Cores. Each GPU presents distinct pros and cons depending on the target enterprise deployment. The primary pros of the Tesla V100 are its exceptional HBM2 memory bandwidth and dominant FP16 half-precision and FP64 deep learning compute speeds; its main cons are its...

Performance Summary The NVIDIA GeForce RTX 3090 delivers top-tier performance for creative professionals and gamers, heavily outperforming the older workstation-class Quadro RTX 8000 in raw computing metrics. Powered by the Ampere architecture, the RTX 3090 nearly doubles CUDA performance and improves ray-tracing speeds by up to 150%. It excels in GPU-bound tasks across machine learning platforms like TensorFlow, as well as 3D rendering engines like Blender, OctaneBench, LuxMark, and V-Ray. Additionally, it serves as an exceptional gaming GPU, maintaining high framerates above 65 FPS in highly demanding titles like Cyberpunk 2077 at 1440p. Professional Limitations & Disadvantages Despite its brute force, the RTX 3090 suffers heavily from artificial software limitations and a lack of optimized drivers for specific enterprise applications. In CAD software benchmarks like Siemens NX, the RTX 3090 delivers less than 5% of the performance of the Quadro RTX 8000 due to deliberate driver segmentation by NVIDIA. Furthermore, the consumer-oriented GeForce driver restricts essential workstation functionalities. It cuts VRAM capacity in half to 24GB compared to the Quadro's 48GB, completely strips away native SR-IOV hardware virtualization support, and...

The ServeTheHome review of the NVIDIA Quadro RTX 8000 praises the workstation GPU as a 9.3/10 rated, high-performance solution for professional AI and rendering workloads, driven by its massive 48GB of GDDR6 ECC memory. Key advantages include the ability to run large-batch AI models locally, excellent performance in SPECviewperf 13, and a blower-style cooler optimized for dense server environments. Conversely, the review notes a steep roughly $5,500 USD price point, making it solely suited for specialized, high-capacity enterprise needs rather than consumer gaming. While powerful, its blower-style cooling can result in thermal constraints that cause it to lag behind the Titan RTX in specific rendering tests, and it lacks a backplate.