Layoutlm Base Uncased
Microsoft's Layoutlm Base Uncased is a multimodal pre-trained model for document image understanding and information extraction tasks, such as form and receipt understanding. It is genuinely best at tasks that involve understanding the layout and format of documents, in addition to text.
Input
Output
Context
1K
Max Output
1K
Parameters
112.6M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Layoutlm Base Uncased
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Layoutlm Base Uncased
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Layoutlm Base Uncased advertises a context window of 512 tokens, with a maximum output of 512 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Layoutlm Base Uncased is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Layoutlm Base Uncased accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Layoutlm Base Uncased is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Layoutlm Base Uncased is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.5 GB at INT8 for the weights and a default context, from the catalog's 112.6M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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