NVIDIA GeForce RTX 3090 Review: BFGPU Benchmarks Unleashed

How We Configured Our Test Systems: We tested the graphics cards represented in this article on an ASUS Prime X299 Deluxe motherboard powered by a Core i9-10980XE 18-core / 36-thread processor and 32GB of HyperX DDR4 RAM clocked at 2,933MHz. The first thing we did when configuring the test system was enter the UEFI and set all values to their "high performance" defaults, then we disabled any integrated peripherals that wouldn't be put to use. The memory's clock was dialed in to its optimal performance settings using its XMP profile and the solid state drive was then formatted and Windows 10 Professional x64 was installed and fully updated. When the Windows installation was complete, we installed all of the drivers, games, applications and benchmark tools necessary to complete our tests.

HotHardware's Test System Intel Core i9 Powered

Hardware Used: Intel Core i9-10980XE (3GHz - 4.4GHz, 18-Core / 36-Thread) ASUS X299 Prime (Intel X299 Chipset) 32GB Corsair DDR4-2933 Samsung SSD 970 EVO Integrated Audio Integrated Network NVIDIA GeForce RTX 3090 FE NVIDIA GeForce RTX 3080 FE NVIDIA Titan RTX NVIDIA GeForce RTX 2080 Ti FE NVIDIA GeForce RTX 2080 Super AMD Radeon RX 5700 XT

Relevant Software: Windows 10 Pro x64 (v2004) AMD Radeon Software v20.8.3 NVIDIA GeForce Drivers v456.16 Benchmarks Used: IndigoBench v4 LuxMark v4_alpha0 SiSoft SANDRA 2020 Blender OctaneBench / OctaneRender VRMark 3DMark (Time Spy, Fire Strike, Port Royal) Unigine Superposition Crytek Neon Noir Metro Exodus Red Dead Redemption 2 Gears Tactics Wolfenstein: Youngblood FarCry: New Dawn

SiSoft SANDRA 2020 Scientific Analysis And Image Processing OpenCL Tests

SANDRA's GPGPU Image Processing benchmark runs through an array of filters on its reference data and offers up an aggregate score, derived from a multitude of individual results. The Scientific Analysis benchmark runs though an array of General Matrix Multiply (GEMM), N-Body Simulations (NBDY), and Fast Fourier Transformation ops and reports the overall speed in FLOPs. CUDA and OpenCL code paths are available in these tests, but we used OpenCL on all cards to keep the playing field level. Previously, using the CUDA code path with NVIDIA GPUs resulted in better performance, but OpenCL actually outperforms it in these tests now...

The Scientific Analysis tests also shows the GeForce RTX 3090 out in front of the GeForce RTX 3080 and Titan RTX, but its performance advantages are somewhat smaller. The RTX 3090 still blows past the Titan RTX, but only leads the RTX 3080 by about 7% - 18% depending on the test.

IndigoBench GPU Rendering Performance

The GeForce RTX 3090 leads the pack in IndigoBench, but by relatively small margins. Here, NVIDIA's new flagship is about 16.6% faster then the RTX 3080 and roughly 31% faster than the Titan RTX. Obviously, the "lower end" cards fall in line after that.

Blender 3D Rendering

Blender is a free, open source 3D content creation tool that can handle everything from modeling, rigging, animation, simulation, rendering, compositing and motion tracking. It's developers have built a handy standalone benchmarking tool that will track the time it takes to rendering a handful of models, using various code paths. We used a couple of publicly available models for the public benchmark, but also ran a more taxing workload to highlight one of the GeForce RTX 3090's strengths.


Moving over to a larger, much more complex model and performing a simultaneous viewport and final renders with Blender tells a totally different story. With this workload, the GeForce RTX 3090's additional compute resources and 24GB frame buffer come into play, and it ends up being roughly 3x - 4x faster than the GeForce RTX 3080 or Titan RTX.

OctaneBench / OctaneRender Testing Rift And Vive Readiness

OTOY's OctaneBench is a standalone tool that gives users the ability to benchmark CPUs or NVIDIA GPUs using the company's OctaneRender engine. This tool leverages the RT cores inside the GPUs when available and runs through a series of models to produce an overall, average score. Like Blender, we also used the actual OctaneRender application to tests a heavier workload than what is included with the standard benchmark.

With OctaneBench, the GeForce RTX 3090 finishes about 20% faster than the GeForce RTX 3080 and nearly 80% faster than the Titan RTX. But what happens when the model being rendered is too big to fit in the graphics card's on-board memory and has to spill out into system memory? Let's find out...

With a large scene rendering, the Titan RTX and GeForce RTX 3080 trade places, and the RTX 3080 falls off a cliff. Because both the Titan RTX and GeForce RTX 3080 have 24GB of RAM on-board, they're able to process this workload much faster, because they don't need to swap and fetch any data from much slower system memory. As a result, both the Titan RTX and GeForce RTX 3090 finish many times faster than the RTX 3080 (about 8x - 9x faster, in fact). Even still, the GeForce RTX 3090 outruns the Titan RTX by over 38%.