In past blog posts, we’ve discussed our goal of developing one or more experimental WebXPRT workloads focused on local, browser-side AI technologies. While many of us regularly interact with cloud-based AI apps and services through a browser, on-device AI capabilities are growing rapidly, and we want WebXPRT to continue to evolve with them.
There are several driving factors behind that growth. Web API technologies keep maturing, giving browsers direct access to the hardware they need for real inference work. Advanced GPU and NPU technology is now widely available in consumer devices, so the local computing power necessary to run AI applications on-device is in reach for many users. And for many organizations, there are compelling reasons to execute increasingly vital work like LLM inferencing and agentic coding tasks on local machines—such as data privacy, regulatory compliance, and cost control.
The reasons for the experimental workload approach
The expansion of on-device AI is exactly the type of shift we built the experimental workload concept to capture. As we shared when we first announced the WebXPRT 5 workload lineup, an experimental workload section gives us the flexibility to put cutting-edge measurement tools in users’ hands—even if those tools won’t yet run on every platform WebXPRT has traditionally supported. Experimental scores stay separate from the main overall score and are completely optional, so we can add tests without affecting comparability or asking anyone to retest. That approach maintains WebXPRT’s strengths while preparing the benchmark for the future—and giving all of us valuable information today.
The AI functions that WebXPRT 5 measures today
WebXPRT 5 already includes four workloads that utilize AI capabilities: Video Background Blur with AI, Detect Faces with AI, Image Classification with AI, and Document Scan with AI. These workloads use machine learning—computer vision and OCR models such as a Caffe-based face detector, SqueezeNet for image labeling, and an LSTM-based OCR engine. WebXPRT’s ability to measure how well devices handle those types of workloads has real value, and it reflects the kinds of light browser-side inference tasks that have been in widespread use for a while.
We recognize, though, that there’s a clear need for more demanding local, browser-based AI workloads—especially LLM inference. We’re targeting that need with our experimental work. Like pretty much everyone else, we’re also developing in the midst of an incredibly dynamic technical environment. We want to purposefully move forward without sacrificing WebXPRT’s stability and reliability for the sake of expedience.
The main decisions we face
Choosing a Web AI framework. We’re still researching our open-source framework options, including candidates like ONNX Runtime Web, Transformers.js, MediaPipe, and TensorFlow.js. The ground here continues to shift. For example, Transformers.js v4 now supports a WebGPU backend and spans a very broad range of model architectures. So, one of our ongoing challenges is picking a durable foundation.
Choosing a web API. Of the primary options we’re investigating, WebGPU now has the broadest browser support (Chrome, Edge, and partial support in Firefox and Safari). WebNN remains the most promising option in the long term because it can directly target NPUs, but it’s still not ready for production—its W3C spec only reached Candidate Recommendation status in early 2026, and browser support outside of flagged, experimental builds isn’t there yet. Our web API outlook hasn’t changed much from before: WebGPU is the most practical path today, and WebNN may be an exciting possibility for tomorrow.
Choosing and sizing workloads. We’ll ideally find workloads demanding enough to genuinely stress new hardware, but light enough to run on slightly older gear without forcing huge model downloads or overextending the test’s runtime. The sweet spot for browser inference today tends to be small, quantized models, and memory ceilings and cold-start downloads are real constraints. Striking the right balance is another part of the challenge we’re working through.
We appreciate your patience
We’ve been talking about experimental WebXPRT AI workloads for a while. While we wish we already had everything worked out, we think the end product will be worth the wait. We appreciate your patience as we work through the details, and we’ll keep updating you here in the blog as we make progress.
As always, we’re open to suggestions. If you have ideas for a browser-based AI workload scenario, a framework or API you think we should weigh, a browser-based AI application you want us to consider, or any other related thoughts, please let us know!
Justin
