How to reduce Responsetimes of Document Intelligence prebuild invoice models using GPU Acceleration?

Hello Olbrich Marc (YIAGI),

Welcome to Microsoft Q&A and Thank you for reaching out.

I understand that you’re looking to reduce the response times of the Document Intelligence Prebuilt Invoice model, especially since you’re seeing longer processing times (over 12 seconds) for multi-page requests on the S0 tier.

Currently, GPU acceleration is not something you can directly enable or configure for Document Intelligence resources. The prebuilt models (such as Invoice, Receipt, and ID) already run on Microsoft’s optimized infrastructure, which includes GPU-backed compute where needed. This optimization is managed automatically by the service and is not exposed as a toggle or deployment setting in the Azure portal.

The response times you are observing are less about GPU enablement and more about how the service is designed and how documents are processed. The S0 tier is a shared environment, so performance may vary depending on document size, page count, and service load. Multi-page PDFs (e.g., 4–5 pages) naturally take longer to process, since layout analysis and field extraction are performed sequentially. At present, there is no “premium GPU” tier that guarantees faster results for prebuilt models.

If faster or more predictable performance is important, you can take several steps to improve it. First, ensure you are calling the most recent API version (e.g., 2024-07-31 GA) and the v4.x prebuilt models, which include performance improvements over earlier versions. Also, confirm that you are using the Standard (S0) tier, since it provides better performance than the free (F0) tier.

Another option is parallelization. Instead of sending one 5-page document, consider splitting pages into smaller sets and processing them in parallel if your workflow allows. This reduces the overall end-to-end latency. You can also simplify the structure of your invoices, since documents with very complex layouts or unnecessary elements can take longer to analyze. For multi-page documents, breaking them into smaller batches may also help.

You should also consider where your resource is deployed. Placing your Document Intelligence resource in the Azure region closest to your users or data source can help reduce network latency. In addition, it’s a good practice to monitor the Azure Service Health dashboard to confirm there are no issues impacting service performance in your region.

Finally, if latency continues to be a blocker for your workloads, you can submit a support ticket or feature request for GPU-accelerated or premium SKUs. Microsoft’s product team is actively gathering this kind of feedback, and it helps shape future service offerings.

GPU acceleration is already built into the backend where needed, but it’s not a setting you can enable. To reduce latency, use the latest models, confirm you’re on the S0 tier, optimize document structure, and use batch or parallel processing.

I Hope this helps. Do let me know if you have any further queries.

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Thank you!