Measure BERT model performance using WASM and WebGPU
Measure execution times of BERT models using WebGPU and WASM
View LLM Performance Leaderboard
Compare and rank LLMs using benchmark scores
Export Hugging Face models to ONNX
View and submit language model evaluations
Merge machine learning models using a YAML configuration file
Benchmark models using PyTorch and OpenVINO
View and submit LLM benchmark evaluations
Analyze model errors with interactive pages
SolidityBench Leaderboard
Merge Lora adapters with a base model
Request model evaluation on COCO val 2017 dataset
The WebGPU Embedding Benchmark is a tool designed to measure the performance of BERT models using WebAssembly (WASM) and WebGPU. It provides a platform to evaluate and compare the efficiency of embedding models across different hardware and software configurations. This benchmark is particularly useful for developers and researchers looking to optimize machine learning workloads in web-based environments.
What is the difference between WebGL and WebGPU?
WebGPU is the successor to WebGL, offering improved performance and better support for modern GPUs. WebGPU provides more efficient memory management and faster computation for machine learning tasks.
Which browsers support WebGPU?
As of now, WebGPU is supported in Chrome, Edge, and Safari Technology Preview. Ensure your browser is up-to-date to run the benchmark effectively.
How can I ensure consistent benchmark results?
To achieve consistent results, run the benchmark in a controlled environment with minimal background processes. Ensure the system's GPU is not under heavy load from other applications.