Pytorch amd gpu benchmark.

Pytorch amd gpu benchmark org metrics for this test profile configuration based on 353 public results since 16 November 2023 with the latest data as of 30 April 2024. sh Graph shows the 7700S results both with the pytorch 2. 13. It utilizes ZLUDA and AMD's HIP SDK to make PyTorch execute code for CUDA device on AMD, with near native performance. Jan 14, 2025 · NVIDIA GPUs tend to be more energy-efficient than AMD GPUs, but the difference can vary depending on the specific workload and software optimization. 4. 41133-dd7f95766 OS: Ubuntu 22. Intel is the new kid on the block, and I would wait to see if the performance and Linux drivers are better than AMD, so I would wait until they are proven. info PyTorch 2. Mar 26, 2025 · Tracking gpu memory for a torch model from pytorch_bench import track_gpu_memory with track_gpu_memory (): # Your GPU operations here pass max_memory = track_gpu_memory. AI researchers and developers using PyTorch with Machine Learning (ML) models and algorithms can now leverage AMD ROCm™ starting with version 5. May 13, 2025 · It covers the steps, tools, and best practices for optimizing training workflows on AMD GPUs using PyTorch features. May 12, 2025 · PyTorch version: 2. AI (and everything surrounding it) is already impacting the world in a variety of ways, and it doesn’t look like it’ll be slowing down in the near future. 0 Introduction presentation and tutorial. 0) Platform Name: AMD Accelerated Parallel Processing Platform Vendor: Advanced Micro Devices, Inc. 7 on Ubuntu® Linux® to tap into the parallel computing power of select AMD Radeon™ GPUs. Here is the link. Austin's own Advanced Micro Devices (AMD) has most generously donated a number of GPU-enabled servers to UT. This section demonstrates how to use the performance-optimized vLLM Docker image for real-world applications, such as deploying an interactive chatbot. In this post, […] What's the state of AMD and AI? I'm wondering how much of a performance difference there is between AMD and Nvidia gpus, and if ml libraries like pytorch and tensorflow are sufficiently supported on the 7600xt. This guide will walk through how to install and configure PyTorch to use Metal on MacOS, explore performance expectations, and discuss this approach's limitations. org metrics for this test profile configuration based on 190 public results since 27 March 2025 with the latest data as of 9 May 2025. 1 405B FP8 model running on 4 AMD GPUs using the vLLM backend server for this Oct 11, 2024 · MI300+ GPUs: FP8 support is only available on MI300 series. . I would like some help understanding the source (i. 0 and ROCm 5. - pytorch/benchmark. Compare AMD vs NVIDIA in performance, software ecosystem, cost, and more. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm. The same unified software stack also supports the CDNA™ GPU architecture of the AMD Instinct™ MI series accelerators. AMD needs to hook up thousands more of MI300X, MI325X to PyTorch CI/CD for automated testing to ensure there is no AMD performance regressions & functional AMD bugs. 9_pytorch_release_2. The release binaries are tested with the recent Linux distributions such as: Nov 7, 2024 · Deep learning GPU benchmarks are critical performance measurements designed to evaluate GPU capabilities across diverse tasks essential for AI and machine learning. py --network <network name> [--batch-size <batch size> ] [--iterations <number of iterations>] [--fp16 <0 or 1> ] [--distributed_dataparallel] [--device_ids <comma separated list (no spaces) of GPU indices (0-indexed) to run distributed_dataparallel api on>] Overview. Nov 21, 2023 · We recently launched AMD ROCm™ 5. AMD Ryzen™ AI software includes the tools and runtime libraries for optimizing and deploying AI inference on AMD Ryzen AI powered PCs 1. Benchmark tool for multiple models on multi-GPU setups. The choice of hardware may depend on the model characteristics, performance, power requirements, and the trade-offs in offloading models to the NPU or integrated GPU. 2 on Linux® to tap into the parallel computing power of the latest high-end AMD Radeon 7000 series desktop GPUs, and based on AMD RDNA 3 GPU architecture. Topics benchmark pytorch windows10 dgx-station 1080ti rtx2080ti titanv a100 rtx3090 3090 titanrtx dgx-a100 a100-pcie a100-sxm4 2060 rtx2060 May 12, 2025 · PyTorch version: 2. Dec 7, 2018 · The bench says about 30% performance drop from the nvidia to the amd, but I’m seeing more like a 85% performance drop ! I’m able to process at full gpu utilization about 9/10 times more batches per second with the nvidia card than with the amd. Jul 3, 2024 · With these considerations in mind, integrating TunableOp into your PyTorch workflow is an easy way to achieve modest performance gains on AMD GPUs without altering existing code and minimal additional effort. Most notably, this new release gives incredible inference performance with Llama 3 70BQ4, and now allows developers to integrated Stable Diffusion (SD) d. Apr 25, 2025 · See the latest AMD post on "Experience the power of PyTorch 2. For more information, see AMD Instinct MI300X system Apr 29, 2025 · It covers the steps, tools, and best practices for optimizing training workflows on AMD GPUs using PyTorch features. Freedom To Customize Feb 9, 2025 · It supports a broad range of AI applications, from vision to NLP. These tools provide GPU developers with the flexibility to optimize GEMM performance, allowing precise fine-tuning for maximum May 13, 2025 · Before running AI workloads, it’s important to validate that your AMD hardware is configured correctly and performing optimally. 35 Python version: 3. 8. 6. cuda is a generic way to access the GPU. PyTorch’s C++ extension. py - Native PyTorch implementation for comparison. We supply a small microbenchmarking script for PyTorch training on ROCm. You can search around for Blender benchmarks. I had to spend $500 on a Nvidia gpu for a new desktop; it being the most expensive part of the built. Jul 1, 2023 · I recently upgraded to a 7900 XTX GPU. I'd stay away from ROCm. Python module can be run directly on Windows, no WSL needed. Lambda's PyTorch® benchmark code is available here. This MPS backend extends the PyTorch framework, providing scripts and capabilities to set up and run operations on Mac. Pytorch/AWS currently has Every year I take a look at this. how the specific kernel is launched), so I can better understand the performance issue. This includes the AMD Instinct™ MI100, the first GPU Jan 1, 2025 · Nvidia will be the best for performance and highest cost with some tinkering needed with the Linux driver. It was a relative success due to Jul 29, 2024 · Artificial Analysis, which has put together a fascinating independent analysis of AI model performance and pricing, published an interesting post on Xitter that had as a thesis that AMD’s “Antares” Instinct MI300X GPU accelerators, announced last December and now shipping, were going to be sitting pretty compared to Nvidia iron when it Feb 5, 2024 · Comparing AMD and NVIDIA GPUs for AI. 05, and our fork of NVIDIA's optimized model implementations. And a link to the code examples here on GitHub. We’ll set up the Llama 3. Anyone else tried this and has any tips? I have a more detailed write-up here: Running PyTorch on the M1 GPU. Can I use AMD GPUs with TensorFlow and PyTorch? Yes, AMD GPUs can be used with popular deep learning frameworks like TensorFlow and PyTorch, thanks to the ROCm platform and HIP API. 0 (zentorch) and IPEX 2. 1-70B, Mixtral-8x7B, Mixtral-8x22B, and Qwen 72B models. 0 software on the systems with 8 AMD Instinct™ MI300X GPUs coupled with Llama 3. 20. Single-GPU fine-tuning and inference describes and demonstrates how to use the ROCm platform for the fine-tuning and inference of machine learning models, particularly large language models (LLMs), on systems with a single GPU Apr 22, 2025 · This document provides guidelines for optimizing the performance of AMD Instinct™ MI300X accelerators, with a particular focus on GPU kernel programming, high-performance computing (HPC), and deep learning operations using PyTorch. PyTorch uses the new Metal Performance Shaders (MPS) backend for GPU training acceleration. AMD GPUs: AMD GPUs are known for their competitive pricing and energy efficiency. If you are running NVIDIA GPU tests, we support Feb 14, 2024 · Comparing Performance: A Detailed Examination. 1-8B model for summarization tasks using LoRA and showcasing scalable training across multiple GPUs. Aug 10, 2023 · *Actual coverage is higher as GPU-related code is skipped by Codecov Install pip install pytorch-benchmark Usage import torch from torchvision. At it's current state, I can only guarantee one thing. Jul 24, 2020 · Completely agree with you about Nvidia’s monopoly. 2_ubuntu20. Apr 4, 2024 · まえがき ROCmを試すためにRadeon Instinct MI50を買ってみて、PyTorchで使えるようにセットアップをしたのが前回。 hashicco. , a GPU holds the model while the sample is on CPU after being loaded from disk or collected as live data). To optimize performance, disable automatic NUMA balancing. Setup# Prerequisites# To follow along with this blog, you will need the following: 8 MI300X AMD GPUs. The natively supported programming languages are HIP (Heterogeneous-Compute Interface for Portability) and OpenCL, but HIP bindings are available May 25, 2022 · (base) davidlaxer@x86_64-apple-darwin13 pytorch-apple-silicon-benchmarks % (base) davidlaxer@x86_64-apple-darwin13 pytorch-apple-silicon-benchmarks % python tests/transformers_sequence_classification. Most ML frameworks have NVIDIA support via CUDA as their primary (or only) option for acceleration. For training, we used a validation split of the wikiText-103-raw-v1 data set, but this can be easily replaced with a train split by downloading the preprocessed and tokenized train file hosted in our repository on Hugging Face Hub . 1 and with pytorch 2. Mar 5, 2025 · With recent PyTorch updates, users can now use MPS to run neural networks and tensor operations directly on a Mac’s M-series chip or AMD GPU. 8 | packaged by conda Aug 28, 2024 · 8xMI300X with 2x AMD EPYC Turin CPU in the Preview category. NVIDIA GPUs: Jan 13, 2025 · Deep learning GPU benchmarks has revolutionized the way we solve complex problems, from image recognition to natural language processing. 163, NVIDIA driver 520. Apr 23, 2024 · Hi, I have collected performance data on MI250X (single GCD) and MI300 AMD GPUs. However, while training these models often relies on high-performance GPUs, deploying them effectively in resource-constrained environments such as edge devices or systems with limited hardware presents unique challenges. xilinx. 2P AMD EPYC 9654 (192 Total Cores, 1536GB Total Memory w/ 24x64GB DIMMs, 2x960GB SSD RAID 1, HT Off, Ubuntu® 22. Apr 21, 2024 · Optimizing PyTorch Performance on AMD GPUs. However, often GPUs cost 3 to 5 times what a cpu would cost. Looking ahead to the next-gen AMD Instinct MI300X GPUs, we expect our PyTorch-based software stack to work seamlessly and continue to scale well. Mar 21, 2025 · On average, a system configured with an AMD Instinct™ MI300X GPU with AITER MHA for prefill shows a14x performance boost, improving Multi-Head Attention (MHA) performance during prefill stages. Access tutorials, blogs, open-source projects, and other resources for AI development with the ROCm™ software platform. Another important difference, and the reason why the results diverge is that PyTorch benchmark module runs in a single thread by default. 8) was made available for AMD GPUs with ROCm 4. , TensorFlow, PyTorch, JAX). May 24, 2022 · (base) davidlaxer@x86_64-apple-darwin13 pytorch-apple-silicon-benchmarks % (base) davidlaxer@x86_64-apple-darwin13 pytorch-apple-silicon-benchmarks % python tests/transformers_sequence_classification. Amd's stable diffusion performance now with directml and ONNX for example is at the same level of performance of Automatic1111 Nvidia when the 4090 doesn't have the Tensor specific optimizations. It also includes Nov 1, 2024 · Out of the various forms of parallelized training, this blog focuses on Distributed Data Parallel (DDP), a key feature in PyTorch that accelerates training across multiple GPUs and nodes. Some may argue this benchmark is unfair to AMD hardware. Mögliche GPU-Leistungsverbesserungen durch Verwendung neuerer PyTorch-Versionen und Funktionen. Testing configuration details: ZD-052: Testing conducted internally by AMD as of 05/15/2023. Using the famous cnn model in Pytorch, we run benchmarks on various gpu. AI DEVELOPMENT WITH PYTORCH ON YOUR DESKTOP Advanced by AMD Radeon™ GPUs and AMD ROCm™ Software Apr 29, 2025 · It covers the steps, tools, and best practices for optimizing training workflows on AMD GPUs using PyTorch features. OpenBenchmarking. But a Feb 17, 2025 · However, Nvidia’s GPUs are still the best GPUs for deep learning due to their well-optimized software ecosystem and widespread framework support (e. 2 (torch. There are no up to date benchmarks, and Passmark results are nit at all representative of NN training performance (because of various DL software specific optimizations). Depending on your system, the Dec 7, 2018 · The bench says about 30% performance drop from the nvidia to the amd, but I’m seeing more like a 85% performance drop ! I’m able to process at full gpu utilization about 9/10 times more batches per second with the nvidia card than with the amd. 0 Torch uses MIOpen, ROCBlas, and RCCL to provide optimal performance on AMD GPUs Pytorch can be installed with ROCm support via pip Use the cuda device type to run on GPUs See full list on aime. 2f} MB") print (f "Current GPU memory used: {current_memory:. 0, cuDNN 8. Getting Started# In this blog, we’ll use the rocm/pytorch-nightly Docker image and build Flash Attention in the container. Optimized GPU Software Stack. 10 docker image with Ubuntu 20. Feb 26, 2025 · Distributed Data Parallel PyTorch Training job on AWS G4ad (AMD GPU) and G4dn (NVIDIA GPU) instances. 0-0001 and 5. Apr 15, 2024 · The unit test confirms our kernel is working as expected. In this blog, we demonstrate that using torch. For more information, see the system validation steps. 0) powered server running AI benchmarks with ZenDNN Plugin for PyTorch 4. 04. This now gives PyTorch developers the ability to build their next great AI solutions leveraging AMD GPU Mar 15, 2024 · PyTorch compilation mode often delivers higher performance, as model operations are fused before runtime, which allows for easy deployment of high-performance kernels. Any supported Linux distributions supported by the version of ROCm you are Researchers and developers working with Machine Learning (ML) models and algorithms using PyTorch, ONNX Runtime, or TensorFlow can now also use ROCm 6. timeit() returns the time per run as opposed to the total runtime like timeit. The release binaries for PyTorch v1. Last I've heard ROCm support is available for AMD cards, but there are inconsistencies, software issues, and 2 - 5x slower speeds. randn (8, 3, 224, 224) # (B, C, H, W) results = benchmark (model, sample, num_runs = 100) Mar 5, 2024 · In the PyTorch framework, torch. This can only access an AMD GPU if one is available. 0 AMD-APP (3137. 0 or above. Now optimized for Llama 3. I see a significant slow down in the following kernels compared to MI250X. 2 software and ROCm 6. Must-Read: AMD Noise Suppression Startup: Uncover the Secrets Behind this Game-Changing Technology You can use AMD GPUs, but honestly, unless AMD starts actually giving a shit about ML, it's always going to be a tedious experience (Can't even run ROCm in WSL ffs). Their open software platform, ROCm, contains the libraries, compilers, runtimes, and tools necessary for accelerating compute-intensive applications on AMD GPUs. 1 Device: CPU - Batch Size: 1 - Model: ResNet-50. Loading. PyTorch benchmark module also provides formatted string representations for printing the results. 0 brings new features that unlock even higher performance, while remaining backward compatible with prior releases and retaining the Pythonic focus which has helped to make PyTorch so enthusiastically adopted by the AI/ML community. PyTorch APIs can also utilize compute and memory partitioning modes through their own multi-device management APIs. This guide demonstrates how to use the AMD Model Automation and Dashboarding (MAD) tool with the ROCm PyTorch container to test inference performance on various models efficiently. Yep, AMD and Nvidia engineers are now in an arm's race to have the best AI performance. 04 it/s for A1111. Further Reading# TunableOp is just one of several inference optimization techniques. In this blog we use Torchtune to fine-tune the Llama-3. We are now ready to benchmark our kernel and assess its performance. May 13, 2025 · The platform also provides features like multi-GPU support, allowing for scaling and parallelization of model training across multiple GPUs to enhance performance. Mar 28, 2025 · Hugging Face hosts the world’s largest AI model repository for developers to obtain transformer models. It&rsquo;s well known that NVIDIA is the clear leader in AI hardware currently. 1-8B, Llama 3. Testing done by AMD on 03/011/2025, results may vary based configuration, usage, software version, and optimizations. Oct 11, 2024 · AMD has just released the latest version of its open compute software, AMD ROCm™ 6. We observed that custom C++ extensions improved a model’s performance compared to a native PyTorch implementation. Benchmark Methodology Apr 14, 2025 · PyTorch (Training Container) – Includes performance-tuned builds of PyTorch with support for advanced attention mechanisms, helping enable seamless LLM training on AMD Instinct MI300X GPUs. 5. These benchmarks measure a GPU’s speed, efficiency, and overall suitability for different neural network models, like Convolutional Neural Networks (CNNs) for image recognition or This article dives into the architectural and performance breakthroughs of the AMD MI300X, explores its benchmarks, and demonstrates how the revolutionary Modular and MAX Platform simplifies AI deployment—with specific emphasis on PyTorch and HuggingFace applications. 0 Clang version: Could not collect CMake version: version 3. Use the ROCm Stack: The ROCm stack is a software platform designed to optimize AMD GPUs for machine learning and high-performance computing. Comparison of learning and inference speed of different GPU with various CNN models in pytorch List of tested AMD and NVIDIA GPUs: Example Results Following benchmark results has been generated with the command: . 0a0+d0d6b1f, CUDA 11. Only 70% of unified memory can be allocated to the GPU on 32GB M1 Max right now, and we expect around 78% of usable memory for the GPU on larger memory. Aug 1, 2023 · With proven platforms gaining momentum, there is significance of a leadership software stack and an optimized ecosystem for achieving application performance. Oct 24, 2024 · Torchtune is a PyTorch library that enables efficient fine-tuning of LLMs. 3+: see the installation instructions. 7 and PyTorch, we are now expanding our client-based ML Development offering, both from the hardware and software side with AMD ROCm 6. And if you look at the specs of the cards, the amd card isn’t supposed to be that worse to me. We have now extended support to include the Radeon™ RX 7900 XT GPU, introducing even more options for AI developers and researchers. 04) 11. This isolation ensures a more accurate representation of the GPU’s computational performance. I tried so hard 10 months ago and it turns out AMD didn't even support the XTX 7900 and weren't even responding to the issues from people posting about it on GitHub. Usually, the sample and model don't reside on the same device initially (e. /show_benchmarks_resuls. set_device(device_id): Sets the default device. Mar 22, 2024 · Pytorch is a python package based on the Torch machine learning library In March 2021, Pytorch (v1. This section will detail the methods used for benchmarking and the resultant performance metrics. Audience: Data scientists and machine learning practitioners, as well as software engineers who use PyTorch/TensorFlow on AMD GPUs. Nov 16, 2023 · PyTorch 2. The stable release of PyTorch 2. Oct 31, 2023 · The AMD Instinct MI25, with 32GB of HBM2 VRAM, was a consumer chip repurposed for computational environments, marketed at the time under the names AMD Vega 56/64. 1+rocm6. I am not at all familiar with the PyTorch source. Otherwise, the GPU might hang until the periodic balancing is finalized. 0 on AMD Solutions" on PyTorch. 1. 31. e. Strategic model-offloading for optimal performance. Disable NUMA auto-balancing. The PyTorch for ROCm training Docker ( rocm/pytorch-training:v25. Conclusion# This blog walks you through an example of using custom PyTorch C++ extensions. An end-to-end application often deploys multiple models running in a pipeline on an AI PC. 7 for the AMD Radeon™ RX 7900 XTX and Radeon™ PRO W7900 GPUs for Machine Learning (ML) development workflows with PyTorch. While it is still true that AMD GPUs do not support as many 3rd party applications as NVIDIA, they do support many popular Machine Learning (ML) applications such as TensorFlow, PyTorch, and AlphaFold, and Molecular Dynamics (MD) applications such as GROMACS, all of which are Number of platforms: 1 Platform Profile: FULL_PROFILE Platform Version: OpenCL 2. Jul 11, 2024 · You can read more about the PyTorch compilation process in PyTorch 2. 0_ubuntu22. Pytorch benchmarks for current GPUs meassured with this scripts are available here: PyTorch 2 GPU Performance Benchmarks We are working on new benchmarks using the same software version across all GPUs. In general, NVIDIA GPUs tend to offer superior performance, especially for computationally intensive tasks such as training large-scale deep learning models or running complex simulations. What's next? We have a lot of exciting features in the pipe for these new AMD Instinct MI300 GPUs. 12 are now compiled with manylinux2014 and they provide compatibility with some older Linux distributions. I think AMD just doesn't have enough people on the team to handle the project. Jan 8, 2025 · AMD GPU: See the ROCm documentation page for supported hardware and operating systems. org which discuss how this partnership enables developers to harness the full potential of PyTorch's capabilities for machine learning, deep learning, and artificial intelligence on AMD's high-performance accelerated platforms. 1 Motivating Examples We show two examples to motivate the necessity of a comprehen-sive benchmark suite for PyTorch. python3 -c . Platform Extensions: cl_khr_icd cl_amd_event_callback Platform Name: AMD Accelerated Parallel Processing Number of devices: 1 Device Type: CL_DEVICE Oct 10, 2024 · 6 MI300-62: Testing conducted by internal AMD Performance Labs as of September 29, 2024 inference performance comparison between ROCm 6. I would argue that a gpu should cost less than a cpu based on the functionalities and performance offered in comparison. By using UCC and UCX, it appeared that mixed-GPU clusters aren’t a distant dream but an Apr 15, 2025 · ROCm provides a robust environment for heterogeneous programs running on CPUs and AMD GPUs. In the future, this project will Dec 10, 2024 · AMD vs NVIDIA: It’s more than just a comparison for your next gaming PC. docker pull packages. (AMD) such as the features, functionality, performance, availability, timing and expected benefits of AMD products including the AMD Instinct™ accelerator family, AMD CDNA™ 4 and AMD CDNA™ “Next”, product roadmaps, leadership AI performance Oct 26, 2023 · PyTorch can use OpenCL for GPU-accelerated computing on AMD GPUs, allowing for better performance and scalability. To run the benchmarks with different CNN models at the PyTorch level, refer the section “PyTorch CNN Benchmarks” on page11. 1 (8B, 70B), Llama 2 (70B), and FLUX. Access Pytorch Training Docker for ROCm and training resources here Docker Container Jan 26, 2024 · We trained our model using the Hugging Face Trainer with a PyTorch backend using an AMD GPU. Useful Links and Blogs. ROCm 6. 6 Device: CPU - Batch Size: 1 - Model: ResNet-50. There is some ubiquity and ease in just using CUDA/nvidia GPU. In this guide, you’ll learn Apr 25, 2025 · Building on our previously announced support of the AMD Radeon™ RX 7900 XT, XTX and Radeon PRO W7900 GPUs with AMD ROCm 5. AMD ROCm™ is an open software stack including drivers, development tools, and APIs that enable GPU programming from low-level kernel to end-user applications. 0. 0, and build the Docker image using the commands below. For more, see LLM Inference Optimizations on AMD GPUs The Optimum-Benchmark is available as a utility to easily benchmark the performance of transformers on AMD GPUs, across normal and distributed settings, with various supported optimizations and quantization schemes. As mentioned above, if you are experimenting with LLMs, stable diffusion, don't get an AMD GPU. AMD MI300X: Architecture and Capabilities Jun 3, 2024 · This press release contains forward-looking statements concerning Advanced Micro Devices, Inc. ROCm supports PyTorch, enabling high-performance execution on AMD GPUs. DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. compile. These new GPUs based on the RDNA 4 architecture join the already-supported Radeon 7000 series built on RDNA 3, further expanding support for high-performance local ML development on Linux®. 61. It also includes This small project aims to setup minimal requirements in order to run PyTorch computatiuons on AMD Radeon GPUs on Windows 10 and 11 PCs as natively as possible. You can be new to machine learning, or experienced in using Jul 11, 2024 · You can read more about the PyTorch compilation process in PyTorch 2. , Llama2) in PyTorch compilation mode, specific layers of the model must be explicitly assigned as compilation targets. When comparing AMD and NVIDIA GPUs for deep learning, performance is a crucial factor to consider. to ("cpu") # Model device sets benchmarking device sample = torch. It delves into specific workloads such as model inference, offering strategies to enhance efficiency. Benchmarks# We use Triton’s benchmarking utilities to benchmark our Triton kernel on tensors of increasing size and compare its performance with PyTorch’s internal gelu function. 7 software stack for GPU programming unlocks the massively parallel compute power of these RDNA™ 3 architecture-based GPUs for use with PyTorch, one of the leading ML frameworks. Performance-optimized vLLM Docker for AMD GPUs. rocm to rocm/pytorch:rocm6. May 13, 2025 · The ROCm PyTorch Docker image offers a prebuilt, optimized environment for testing model inference performance on AMD Instinct™ MI300X series accelerators. Dec 22, 2024 · Tensorwave, the largest AMD GPU Cloud has given GPU time for free to a team at AMD to fix software issues, which is insane given they paid for the GPUs. Note: For Apple Silicon, check the recommendedMaxWorkingSetSize in the result to see how much memory can be allocated on the GPU and maintain its performance. For maximum MI300X GPU performance on systems with AMD EPYC™ 9004-series processors and AMI System BIOS, the following configuration of system BIOS settings has been validated. Supported AMD GPU: see the list of compatible GPUs. That being said, the Jan 21, 2025 · To understand the performance differences between CPU and GPU using PyTorch, we will explore several benchmarks. 76 it/s for 7900xtx on Shark, and 21. To execute: python micro_benchmarking_pytorch. A client solution built on powerful high-end AMD GPUs enables a local Dec 15, 2023 · As shown above, performance on AMD GPUs using the latest webui software has improved throughput quite a bit on RX 7000-series GPUs, while for RX 6000-series GPUs you may have better luck with Oct 31, 2023 · The latest AMD ROCm 5. 2. benchmark. Jan 19, 2024 · Benchmarking ROCrand against CUDA on an Nvidia V100 GPU reveals a 30–50% performance deficit on real workloads like raytracing. Since the original ROCm release in 2016, the ROCm platform has evolved to support additional libraries and tools, a wider set of Linux® distributions, and a range of new GPUs. py --device cpu --pre_trained_name bert-base-cased --mode inference --steps 100 --sequence_length 128 --batch_size 4 Some weights of the model Apr 15, 2025 · ROCm provides a robust environment for heterogeneous programs running on CPUs and AMD GPUs. Triton is a Python based DSL (Domain Specific Language), compiler and related tooling designed for writing efficient GPU kernels in a hardware-agnostic manner, offering high-level abstractions while enabling low-level performance optimization for AI and HPC workloads. To optimize the performance of PyTorch on AMD GPUs, consider the following tips: 1. Application Example: Interactive Chatbot. Docker: See Install Docker Engine on Ubuntu for installation instructions. Jul 31, 2023 · Although this is our first look at Stable Diffusion performance, what is most striking is the disparity in performance between various implementations of Stable Diffusion: up to 11 times the iterations per second for some GPUs. Consistent API PyTorch aims to provide a consistent API for device management, so the methods you use for NVIDIA GPUs will generally work similarly for AMD GPUs with ROCm. com 今回は取ったベンチマークの結果をご紹介！ まとめ ROCmは本当にほぼコード変更無しでCUDA用のTensorFlow、PyTorch、Transformersのコードが動く。素晴らしい。 1GPUであればMI50 The performance gap between the 4080 and XTX is pretty huge, especially considering the XTX is suppose to be equivalent to the 4080 in it's performance. The Linux rocm benchmark performance will not be attainable for amd consumer cards for most normal users and even developers will have challenges with maintaining an installation with them long term due to amds lack of support. Before running AI workloads, it’s important to validate that your AMD hardware is configured correctly and performing optimally. PyTorch training container optimized for AMD GPUs. 3. ROCm is AMD’s open source software platform for GPU-accelerated high performance computing and machine learning. - microsoft/DirectML Oct 30, 2024 · Use the following procedures to reproduce the benchmark results on an MI300X accelerator with the prebuilt vLLM Docker image. Feb 6, 2025 · Given the pivotal role of GEMM operations in AI workloads, particularly for LLM applications, AMD offers a suite of powerful tuning tools, including rocblas-gemm-tune, hipblaslt-bench, and PyTorch TuneableOps. Apr 2, 2025 · Table 1: The system configuration used in measuring the performance of Llama 2 70B benchmark In the following performance chart, we show the performance results of the MI325X compared with the Nvidia H200 on Llama 2 70B offline and server submissions, submission IDs 5. We conducted benchmarks on a system with dual AMD EPYC 7713 64-Core Processors, 1 TB RAM, and a single AMD MI250 GPU to handle the matrix multiplication. 5 ) image provides a prebuilt optimized environment for fine-tuning and pretraining a model on AMD Instinct MI325X and MI300X May 29, 2024 · PyTorch Profiler is a performance analysis tool that enables developers to examine various aspects of model training and inference in PyTorch. 2, clone the vLLM repository, modify the BASE_IMAGE variable in Dockerfile. To get started, let’s pull it. The AI Developer Hub contains AMD ROCm tutorials for training, fine-tuning, and inference. AMD Radeon™ RX 9060 XT. 2 Is debug build: False CUDA used to build PyTorch: N/A ROCM used to build PyTorch: 6. 3 which supports Radeon GPUs on native Ubuntu® Linux® systems. Detailed Llama-3 results Run TGI on AMD Instinct MI300X Eine Übersicht der Leistung von PyTorch auf den neuesten GPU-Modellen. 5 LTS (x86_64) GCC version: (Ubuntu 11. Understanding the per-formance difference across various architectures is one of the ma- TorchBench is a collection of open source benchmarks used to evaluate PyTorch performance. May 15, 2024 · PyTorch 2. torch. hatenablog. timeit() does. max_memory current_memory = track_gpu_memory. 2 compared to Native PyTorch Compile 2. These settings must be used for the qualification process and should be set as default values in the system BIOS. Hugging Face models and tools significantly enhance productivity, performance, and accessibility in developing and deploying AI solutions. The best GPUs for machine learning should have high compatibility with these ML frameworks, as a mismatch can lead to inefficiencies in acceleration, driver and Oct 30, 2023 · Thanks to PyTorch's support for both CUDA and ROCm, the same training stack can run on either NVIDIA or AMD GPUs with no code changes. 12. 2 Libc version: glibc-2. Apr 26, 2025 · The device is set to "cuda" in both GPU availability cases, highlighting the consistent PyTorch API for both NVIDIA and AMD GPUs. Besides being great for gaming, I wanted to try it out for some machine learning. compile can speed up real-world models on AMD GPU with ROCm by evaluating the performance of various models in Eager-mode and different modes of torch. The 2023 benchmarks used using NGC's PyTorch® 22. 0 represents a significant step forward for the PyTorch machine learning framework. Jun 30, 2023 · With the release of PyTorch 2. This blog was tested on a machine with 8 AMD Instinct MI210 GPUs. 1+: See the ROCm installation for Linux for installation instructions. 4, we are excited to announce that LLM training works out of the box on AMD MI250 accelerators with zero code changes and at high performance! Jan 5, 2025 · Discover the best GPU for machine learning in 2025. ROCm supports various programming languages and frameworks to help developers access the power of AMD GPUs. 2. com/instinct-china/dev-benchmark-300x:rocm6. 2f} MB Dec 17, 2024 · In a prior blog post, we provided an overview of the Triton language and its ecosystem. AMD Radeon™ RX 9070 GRE. 10_pytorchtraining_v253 May 15, 2025 · AMD Radeon Graphics (Ryzen 7000) SB55_OCS3: 2645: AMD Radeon Graphics (Ryzen 7000) SB55_OCS2: 2622: Intel Core i9-11980HK: SB65_Stock: 2575: Intel Core i5-12400: SB37_Stock: 2551: Intel Core i9-11900H: WSL_DIRECTML: 2240: Intel UHD Graphics 770 (13th Gen) SB57_OCS4: 2072: AMD Radeon Graphics (Ryzen 7000) SB55_OCS1: 2057: Intel UHD Graphics 770 AMD Radeon™ RX 9070 XT. Single-GPU fine-tuning and inference describes and demonstrates how to use the ROCm platform for the fine-tuning and inference of machine learning models, particularly large language models (LLMs), on systems with a single GPU Jul 21, 2020 · Update: In March 2021, Pytorch added support for AMD GPUs, you can just install it and configure it like every other CUDA based GPU. py - Trainer file to test PyTorch vs. 1. AMD’s Radeon Instinct series is specifically designed for AI applications and offers features like the Infinity Fabric Link for high-speed interconnects. 0-1ubuntu1~22. Timer. Benchmarks zu Training von LLMs und Bildklassifizierung. 8 | packaged by conda Feb 9, 2025 · PyTorch Fully Sharded Data Parallel (FSDP) is a data parallelism technique that enables the training of large-scale models in a memory-efficient manner. These benchmarks measure a GPU’s speed, efficiency, and overall suitability for different neural network models, like Convolutional Neural Networks (CNNs) for image recognition or Sep 5, 2024 · Overview. Installing the ROCm stack can improve the performance of PyTorch on AMD GPUs. Misleading performance characterization. Make an informed decision for your ML projects. It allows users to collect and analyze detailed profiling information, including GPU/CPU utilization, memory usage, and execution time for different operations within the model. PyTorch is a key part of AMD’s AI journey, and AMD’s Victor Peng, AMD President and Soumith Chintala, founder of PyTorch discussed the latest progress at the DC & AI Keynote on June 12. py --device cpu --pre_trained_name bert-base-cased --mode inference --steps 100 --sequence_length 128 --batch_size 4 Some weights of the model Apr 16, 2024 · mlp_train. g. 0-0060, respectively. The MPS framework optimizes compute performance with kernels that are fine-tuned for the unique characteristics of each Metal GPU family. Installation# To access the latest vLLM features in ROCm 6. May 13, 2025 · PyTorch is an open-source machine learning framework that is widely used for model training with GPU-optimized components for transformer-based models. It leverages popular machine learning frameworks on AMD GPUs. May 21, 2024 · Because it’s always important to be able to replicate and challenge a benchmark, we are releasing a companion Github repository containing all the artifacts and source code we used to collect performance showcased in this blog. models import efficientnet_b0 from pytorch_benchmark import benchmark model = efficientnet_b0 (). NVIDIA offered the highest performance on Automatic 1111, while AMD had the best results on SHARK, and the highest-end AMD ROCm software allows developers the freedom to customize and tailor their GPU software for their own needs encouraging community collaboration. 04_py3. Compatible to CUDA (NVIDIA) and ROCm (AMD). Sep 11, 2023 · Goal: The machine learning ecosystem is quickly exploding and we aim to make porting to AMD GPUs simple with this series of machine learning blogposts. This site provides an end-to-end journey for all AI developers who want to develop AI applications and optimize them on AMD GPUs. Single-GPU fine-tuning and inference describes and demonstrates how to use the ROCm platform for the fine-tuning and inference of machine learning models, particularly large language models (LLMs), on systems with a single GPU Mar 21, 2022 · Since 2006, AMD has been developing and continuously improving their GPU hardware and software technology for high-performance computing (HPC) and machine learning. This entry showed how AMD’s next generation of CPU improves performance of AI tasks. The natively supported programming languages are HIP (Heterogeneous-Compute Interface for Portability) and OpenCL, but HIP bindings are available Apr 16, 2024 · mlp_train. cuda. The GPU performance was 2x as fast as the CPU performance on the M1 Pro, but I was hoping for more. many PyTorch performance bugs or fairly evaluate the performance impact of patches. 1-dev. AMD, along with key PyTorch codebase developers (including those at Meta AI), delivered a set of updates to the ROCm™ open software ecosystem that brings stable support for AMD Instinct™ accelerators as well as many Radeon™ GPUs. Support for Hugging Face models and tools on Radeon GPUs using ROCm, allowing users to unlock the full potential of LLMs on their desktop systems. The rocm repos are also a disaster and impossible to get much help for rocm issues or contribute prs for. 04, PyTorch® 1. Vergleich der GPU-Leistung und Skalierbarkeit auf Multi-GPU Systemen. OpenCL has not been up to the same level in either support or performance. The benchmarks include training a deep learning model, performing inference, and handling different data sizes. It's pretty cool and easy to set up plus it Performance testing for GPUs (Nvidia, AMD, single card) on CUDA platforms using a collection of classic deep learning models based on PyTorch. The Radeo Sep 5, 2024 · Overview. Ryzen AI software enables applications to run on the neural processing unit (NPU) built in the AMD XDNA™ architecture, the first dedicated AI processing silicon on a Windows x86 processor 2, and supports an integrated GPU (iGPU). 04, GCC 11. trainer. Don't know about PyTorch but, Even though Keras is now integrated with TF, you can use Keras on an AMD GPU using a library PlaidML link! made by Intel. Crazy! PyTorch 2. To run an LLM decoder model (e. compile) throughput Apr 25, 2025 · See the latest AMD post on "Experience the power of PyTorch 2. current_memory print (f "Max GPU memory used: {max_memory:. AMD will be slower and lower cost without the tinkering needed with the Linux driver. FSDP achieves this memory efficiency by sharding model parameters, optimizer states, and/or gradients across GPUs, reducing the memory footprint required by each GPU. This blog demonstrates how to speed up the training of a ResNet model on the CIFAR-100 classification task using PyTorch DDP on AMD GPUs with ROCm. Performance testing for SophgoTPU (single card) using a collection of classic deep learning models in bmodel format. smys vglwx smdwco ypcbii jlhp dgm gkuuj eii ngv ocghv