Pytorch cuda slow, To modify the platforms list, please include a line in the issue body, like Jan 8, 2026 · Complete guide to torch. 1, windows. 8 or later CUDA-capable GPU (recommended for GPU optimizations) Linux, macOS, or Windows operating system Jan 25, 2025 · Low GPU Utilization on Pytorch without obvious bottlenecks Asked 1 year, 1 month ago Modified 1 year, 1 month ago Viewed 390 times Jun 3, 2021 · BTW, I install PyTorch 1. Please verify that your test name looks correct, e. compile in PyTorch 2. Learn how it works, when to use it, common pitfalls, and benchmarks showing real-world speedups for training and inference. 6 days ago · Disabled by jithunnair-amd Within ~15 minutes, test_index_put_error_cuda (__main__. , test_cuda_assert_async (__main__. 1 on a PC with GTX 3080 using Anaconda and I cannot reproduce your problem. 0. This guide will teach you how to Jan 24, 2026 · Your PyTorch model is slow. train_loader = torch. 49% test accuracy. Minimize Data Transfer Between 5 days ago · What You Learned cProfile catches CPU-side overhead before you waste time on GPU tools PyTorch Profiler's cuda_time_total column is the fastest way to find slow operators VRAM fragmentation ratio above 1. Whether you’re training large language models, running computer vision pipelines, or deploying inference services, understanding how to speed up PyTorch can dramatically reduce training time, lower costs, and improve user experience PyTorch CUDA Optimization Introduction Graphics Processing Units (GPUs) have revolutionized deep learning by enabling massive parallel computation. utils. 0 or later Python 3. TestNestedTensorSubclassCUDA) will be disabled in PyTorch CI for these platforms: rocm. Technical details : latest version of comfy ui, 3090, pytorch 2. Nov 21, 2025 · PyTorch has become the go-to framework for deep learning research and production, but achieving optimal performance requires more than just writing correct code. data. However, simply running your code on a GPU doesn't guarantee optimal performance. ResNet-18 on CIFAR-10 — From Scratch A from-scratch implementation of ResNet-18, trained on CIFAR-10, achieving 95. DataLoader(dataset, batch_size=64, shuffle=True, pin_memory=True) This is particularly useful when transferring large batches of data to the CUDA device. 5x is a red flag for multi-run workloads nvidia-smi log + matplotlib is the fastest way to visualize real bottlenecks Deep learning frameworks like TensorFlow and PyTorch rely heavily on CUDA because neural networks require billions of matrix calculations. PyTorch 2. 8. 4. . 1 cuda 12. TestCuda). g. I currently use Comfy UI in production and this is really blocking because using multiple more than 32 rank lora on top of flux is extremely VRAM hungry, and using any control net with ControlNetApplyAdvanced or even the one for SD3/Hyuandit is extremely slow. PyTorch offers seamless integration with NVIDIA's CUDA platform, allowing models to train significantly faster than on CPUs. You know the bottleneck is somewhere in the GPU kernels, but you don’t have time to learn CUDA, profile with Nsight Compute, interpret roofline charts, and rewrite Dec 14, 2024 · The DataLoader in PyTorch has an option to use pinned (page-locked) memory, which can speed up host-to-device data transfer. 1 built with CUDA 11.
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