102424machinelearningtest

Intel Core i9-12900K testing with a ASUS PRIME Z790-V AX (1802 BIOS) and ASUS NVIDIA w Dual GeForce RTX 3090 24GB w NVLink on Ubuntu 24.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2410281-NE-102424MAC72

Jump To Table - Results

ASUS NVIDIA GeForce RTX 3090

Processor: Intel Core i9-12900K @ 5.10GHz (16 Cores / 24 Threads), Motherboard: ASUS PRIME Z790-V AX (1802 BIOS), Chipset: Intel Raptor Lake-S PCH, Memory: 96GB, Disk: 2000GB Samsung SSD 970 EVO Plus 2TB, Graphics: ASUS NVIDIA GeForce RTX 3090 24GB, Audio: Intel Raptor Lake HD Audio, Monitor: S24F350, Network: Realtek RTL8111/8168/8211/8411 + Realtek Device b851

OS: Ubuntu 24.04, Kernel: 6.8.0-47-generic (x86_64), Desktop: GNOME Shell 46.0, Display Server: X Server + Wayland, Display Driver: NVIDIA 560.35.03, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.6.65, Compiler: GCC 13.2.0 + CUDA 12.5, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Environment Notes: PRIMUS_libGLa=/usr/lib/nvidia-current/libGL.so.1:/usr/lib32/nvidia-current/libGL.so.1:/usr/lib/x86_64-linux-gnu/libGL.so.1:/usr/lib/i386-linux-gnu/libGL.so.1 PRIMUS_libGLd=/usr/$LIB/libGL.so.1:/usr/lib/$LIB/libGL.so.1:/usr/$LIB/mesa/libGL.so.1:/usr/lib/$LIB/mesa/libGL.so.1
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0x37 - Thermald 2.5.6
Graphics Notes: BAR1 / Visible vRAM Size: 32768 MiB - vBIOS Version: 94.02.4b.00.0b
OpenCL Notes: GPU Compute Cores: 10496
Python Notes: Python 3.12.3
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Mitigation of Clear Register File + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; RSB filling; PBRSB-eIBRS: SW sequence; BHI: BHI_DIS_S + srbds: Not affected + tsx_async_abort: Not affected

SHOC Scalable HeterOgeneous Computing

The CUDA and OpenCL version of Vetter's Scalable HeterOgeneous Computing benchmark suite. SHOC provides a number of different benchmark programs for evaluating the performance and stability of compute devices. Learn more via the OpenBenchmarking.org test page.

LeelaChessZero

oneDNN

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

DeepSpeech

Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.

R Benchmark

This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.

RNNoise

RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

Neural Magic DeepSparse

This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./deepsparse: 2: /.local/bin/deepsparse.benchmark: not found

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream

Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream

Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream

spaCy

The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tqdm'

Caffe

This is a benchmark of the Caffe deep learning framework and currently supports the AlexNet and Googlenet model and execution on both CPUs and NVIDIA GPUs. Learn more via the OpenBenchmarking.org test page.

Model: AlexNet - Acceleration: CPU - Iterations: 100

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./caffe: 3: ./tools/caffe: not found

Model: AlexNet - Acceleration: CPU - Iterations: 200

Model: AlexNet - Acceleration: CPU - Iterations: 1000

Model: GoogleNet - Acceleration: CPU - Iterations: 100

Model: GoogleNet - Acceleration: CPU - Iterations: 200

Model: GoogleNet - Acceleration: CPU - Iterations: 1000

Mobile Neural Network

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

Target: CPU - Model: DenseNet

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./tnn: 3: ./test/TNNTest: not found

Target: CPU - Model: MobileNet v2

Target: CPU - Model: SqueezeNet v2

Target: CPU - Model: SqueezeNet v1.1

XNNPACK

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

FP16: No - Mode: Inference - Network: VGG16 - Device: CPU

FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU

OpenVINO

This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

Benchmark: P1B2

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'

Benchmark: P3B1

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'

Benchmark: P3B2

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

Detector: KNN CAD

Detector: Relative Entropy

Detector: Windowed Gaussian

Detector: Earthgecko Skyline

Detector: Bayesian Changepoint

Detector: Contextual Anomaly Detector OSE

ONNX Runtime

Model: GPT-2 - Device: CPU - Executor: Parallel

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "GPT2/model.onnx" failed: No such file or directory

Model: GPT-2 - Device: CPU - Executor: Standard

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Model: bertsquad-12 - Device: CPU - Executor: Parallel

Model: bertsquad-12 - Device: CPU - Executor: Standard

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Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

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Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard

AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

Benchmark: scikit_ica

Benchmark: scikit_qda

Benchmark: scikit_svm

Benchmark: scikit_linearridgeregression

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Glmnet

Benchmark: Plot Lasso Path

Benchmark: Plot Fast KMeans

Benchmark: Isotonic / Pathological

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

Benchmark: RCV1 Logreg Convergencet

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: IndexError: list index out of range

Benchmark: Plot Singular Value Decomposition

Benchmark: Plot Non-Negative Matrix Factorization

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: KeyError:

Whisper.cpp

Llama.cpp

Model: llama-2-7b.Q4_0.gguf

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: main: error: unable to load model

Model: llama-2-13b.Q4_0.gguf

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: main: error: unable to load model

Model: llama-2-70b-chat.Q5_0.gguf

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: main: error: unable to load model

Llamafile

Test: llava-v1.5-7b-q4 - Acceleration: CPU

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./run-llava: line 2: ./llava-v1.6-mistral-7b.Q8_0.llamafile.86: No such file or directory

Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./run-mistral: line 2: ./mistral-7b-instruct-v0.2.Q5_K_M.llamafile.86: No such file or directory

Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU

ASUS NVIDIA GeForce RTX 3090: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./run-wizardcoder: line 2: ./wizardcoder-python-34b-v1.0.Q6_K.llamafile.86: No such file or directory

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

256 Results Shown

ASUS NVIDIA GeForce RTX 3090

Testing initiated at 25 October 2024 01:23 by user spencer.

102424machinelearningtest

Statistics

Graph Settings

Multi-Way Comparison

Table

Run Management

ASUS NVIDIA GeForce RTX 3090

SHOC Scalable HeterOgeneous Computing

LeelaChessZero

oneDNN

Numpy Benchmark

DeepSpeech

R Benchmark

RNNoise

TensorFlow Lite

PyTorch

TensorFlow

Neural Magic DeepSparse

spaCy

Caffe

Mobile Neural Network

NCNN

TNN

XNNPACK

PlaidML

OpenVINO

ECP-CANDLE

Numenta Anomaly Benchmark

ONNX Runtime

AI Benchmark Alpha

Mlpack Benchmark

Scikit-Learn

Whisper.cpp

Llama.cpp

Llamafile

OpenCV

256 Results Shown

ASUS NVIDIA GeForce RTX 3090