![]() ![]() ![]() We're nearly finished retesting all of the ray-tracing capable GPUs on a slightly revamped test suite, using a Core i9-13900K instead of a Core i9-12900K. Current GPU prices are slowly trending down as well, though the new cards are all holding relatively steady. Whether it's playing games, running artificial intelligence workloads like Stable Diffusion, or doing professional video editing, your graphics card typically plays the biggest role in determining performance - even the best CPUs for Gaming take a secondary role. The Google IREE team is focused on community-driven development of the core technology and is happy to enable industry partners like Nod.ai to pursue specific, high value integrations on behalf of their customers.Our GPU benchmarks hierarchy ranks all the current and previous generation graphics cards by performance, and Tom's Hardware exhaustively benchmarks current and previous generation GPUs, including all of the best graphics cards. SHARK is built on pre-release IREE, downstream Nod enhancements and Nod.ai Codegen Search. We would specifically like to acknowledge the IREE team (Ben Vanik, Thomas Raoux, Mahesh Ravishankar, Hanhan Wang, Nicolas Vasilache, Tobias Gysi, Sean Silva, Stella Laurenzo, Yi Zhang ) for their continued support and for building a world class compiler and runtime. The Nod.ai team works very closely with the broader LLVM/MLIR community, the Google IREE community and the torch-mlir community without whom this won’t be possible. To generate the numbers for SHARK – you can uninstall the IREE pip packages and build SHARK from this branch here. run_benchmark.sh script will generate the baseline numbers with the latest nightly build of PyTorch/Torchscript, ONNXRuntime, Tensorflow/XLA and Google IREE. To back up the claims of being 3X faster than other runtimes we have open sourced all the benchmarks here. All the experiments were done on a A2-HIGHGPU-1G Google Cloud VM and independently verified on a DGX A100. In a follow on post we will compare with those fusions enabled, but we will need to measure accuracy too since the numerical approximations affect the quality of predictions. We disable these approximations such as Fast_GELU approximation in this benchmark but even with those enabled SHARK is faster than ONNXRuntime. ONNXRuntime has a set of numerical approximations that bring its speed to 1.9ms for the same workload. We had done a previous blog post on Infinity here. We chose a Batch Size 1 and sequence length of 128 which is more representative of actual inference workloads. In this benchmark we lower down to SHARK via the mhlo exporter. This was primarily chosen so we can compare against Huggingface Infinity which used a similar model. We selected the BERT microsoft/MiniLM-L12-H384-uncased from Huggingface for our benchmarks. Sign up here today for early access – it is free to try and there is nothing to pay if you don’t see a performance improvement in your ML deployment. ![]() SHARK extends its performance across CPUS/GPUs and accelerators. SHARK can be seamlessly integrated as part of your larger MLOps workflow. There is no need for you to modify your training or inference code. SHARK is seamlessly integrated with Pytorch via Torch-mlir, though SHARK can work across various Machine Learning Frameworks if required. Whether you are using Docker, Kubernetes or plain old `pip install` we have an easy to deploy solution of SHARK for you – on-premise or in the cloud. All of this is available to deploy seamlessly in minutes. Introducing SHARK – A high performance PyTorch Runtime that is 3X faster than the PyTorch/Torchscript, 1.6X faster than Tensorflow+XLA and 76% faster than ONNXRuntime on the Nvidia A100. ![]()
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