Gpu benchmark list: PassMark Software — Video Card (GPU) Benchmarks

11 декабря, 2022

GPU Benchmarks for Deep Learning

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GPU training/inference speeds using PyTorch/TensorFlow for computer vision (CV), NLP, text-to-speech (TTS), etc.

Visualization

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Precision

Number of GPUs

Model

Visualization

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Precision

Number of GPUs

Model

Visualization

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Precision

Methods

Model

To measure the relative effectiveness of GPUs when it comes to training neural networks we’ve chosen training throughput as the measuring stick. Training throughput measures the number of samples (e.g. tokens, images, etc…) processed per second by the GPU.

Using throughput instead of Floating Point Operations per Second (FLOPS) brings GPU performance into the realm of training neural networks. Training throughput is strongly correlated with time to solution — since with high training throughput, the GPU can run a dataset more quickly through the model and teach it faster.

In order to maximize training throughput it’s important to saturate GPU resources with large batch sizes, switch to faster GPUs, or parallelize training with multiple GPUs. Additionally, it’s also important to test throughput using state of the art (SOTA) model implementations across frameworks as it can be affected by model implementation.

TensorFlow

We are working on new benchmarks using the same software version across all GPUs. Lambda’s TensorFlow benchmark code is available here.

The RTX A6000 was benchmarked using NGC’s TensorFlow 20.10 docker image using Ubuntu 18.04, TensorFlow 1.15.4, CUDA 11.1.0, cuDNN 8.0.4, NVIDIA driver 455.32, and Google’s official model implementations.

The A100s, RTX 3090, and RTX 3080 were benchmarked using Ubuntu 18.04, TensorFlow 1.15. 4, CUDA 11.1.0, cuDNN 8.0.4, NVIDIA driver 455.45.01, and Google’s official model implementations.

Pre-ampere GPUs were benchmarked using TensorFlow 1.15.3, CUDA 10.0, cuDNN 7.6.5, NVIDIA driver 440.33, and Google’s official model implementations.

PyTorch

We are working on new benchmarks using the same software version across all GPUs. Lambda’s PyTorch benchmark code is available here.

The RTX A6000, A100s, RTX 3090, and RTX 3080 were benchmarked using NGC’s PyTorch 20.10 docker image with Ubuntu 18.04, PyTorch 1.7.0a0+7036e91, CUDA 11.1.0, cuDNN 8.0.4, NVIDIA driver 460.27.04, and NVIDIA’s optimized model implementations.

Pre-ampere GPUs were benchmarked using NGC’s PyTorch 20.01 docker image with Ubuntu 18.04, PyTorch 1.4.0a0+a5b4d78, CUDA 10.2.89, cuDNN 7.6.5, NVIDIA driver 440.33, and NVIDIA’s optimized model implementations.

YoloV5

YOLOv5 is a family of SOTA object detection architectures and models pretrained by Ultralytics. We use the opensource implementation in this repo to benchmark the inference lantency of YOLOv5 models across various types of GPUs and model format (PyTorch, TorchScript, ONNX, TensorRT, TensorFlow, TensorFlow GraphDef). Details for input resolutions and model accuracies can be found here.

GPU servers benchmark and graphics card comparison Chart 2022

Performance comparison of servers equipped with GPU cards — Comparison Chart and Ranking List

20.06.2022

GPU Benchmarks

GPU servers

Multithreaded encoding: Pay twice as much or go for built-in?

Will we be able to multiply the performance with a professional video card, which costs twice as much? Let’s check it out.

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03.06.2022

GPU Benchmarks

GPU servers

Dedicated servers

NVIDIA A5500: real power or just a facelift

Testing the new NVIDIA RTX5500 professional GPU card for tasks related to encoding, machine learning, rendering.

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03.03.2022

GPU servers

GPU Benchmarks

Testing multi-threaded video distribution on gaming GPUs

When working with streaming video, the quality and speed of playback are key. Is it possible to set up multi-stream broadcasting without buying expensive hardware? Let’s see what we can do

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19.09.2021

GPU servers

GPU Benchmarks

NVIDIA RTX A5000, A4000, RTX 3090 and Quadro RTX 4000 video cards benchmark

We conducted our own price-performance testing of the NVIDIA RTX A5000 and A4000 professional graphics cards and compared them with the RTX 3090 and Quadro RTX 4000

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07. 04.2021

GPU servers

GPU Benchmarks

Comparative testing of GPU servers with new NVIDIA RTX30 video cards in AI / ML tasks

Based on the results of testing new graphics solutions of the GeForce RTX 30 family, we can confidently assert that NVIDIA has brilliantly coped with the task of releasing affordable graphics cards with tensor cores that are powerful enough for fast AI computing.

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List of utilities for tuning, overclocking and testing PC

This material was written by a site visitor and has been rewarded.

Buying a new computer, upgrading hardware, or just looking to fine-tune your PC? Start by choosing the right software for overclocking and tuning. In this article, we will look at the most popular and convenient tools for monitoring temperatures, voltages and power consumption, as well as software for testing the stability of settings and benchmarks.

Part 1. Information about the computer

CPU-Z — shows all the necessary information, firstly, about the processor (stepping, frequency, multiplier), secondly, about the motherboard, BIOS version and even knows how to compare CPU power using the built-in benchmark.

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GPU-Z — all the same, only about the video card. In addition to data on the graphics chip and memory chips, the utility can show data from temperature sensors, voltages and power consumption. There is also a built-in test to check the video card for overclocking.

HWInfo — in the right hands, this is the only adequate tool for simultaneous monitoring of any PC data. Processor frequency, all voltages, fan speeds, RAM timings, all information about the video card in one window. Has a fully customizable interface.

AIDA64 is also one of the most powerful hardware monitoring tools, but not as convenient as the above utilities. However, overclockers love this program for a few exclusive tests. Namely, RAM tests and system stability tests.

Part 2: Stability testing.

LinX is the most demanding test for components. Literally roasting for PC. However, it shows well the weaknesses in cooling, the lack of voltage, not only on the processor, but also on the RAM too.

Prime95 — excellent stability test, less demanding on hardware reliability, however, it also tests longer than LinX.

OCCT is an equally high-quality utility for checking CPU overclocking for stability. Able to test and monitor at the same time.

The FurMark is an ancient tool, but still relevant. Tests the video card on a par with LinX for the processor. Heats up as much as possible, use only with good cooling.

TestMem5 is by far one of the best tools for testing RAM overclocking. Of course, if you use popular profiles from anta777, which can be found in the topic of overclocking RAM.

Part 3. Benchmarks.

Cinebench R20 is the main CPU overclocker benchmark. Sets the processor to a real task for rendering virtual models. The utility is used by CPU manufacturers for a visual comparison of products in tasks close to real ones.

3DMark is the main benchmark for testing the performance of the entire platform, but with an emphasis on graphics. He also knows how to test for stability and a lot of interesting things.

Geekbench 5 is a new version of the well-known comprehensive PC benchmark. It also has a set of tasks similar to real ones.

CrystalDiskMark — where without testing the speed of disks. The interface is extremely clear, comments are not needed.

This set of utilities is enough to set up the main components of a turnkey PC and test the performance of an overclocked system.

This material was written by a site visitor and has been rewarded.