Can we run document AI on-premise without sending files to the cloud?

Yes, document AI can run entirely on-premise, keeping every file inside a company's own network, by deploying open-weight OCR and vision-language models on locally owned or colocated GPU servers instead of calling a cloud API. Open-weight vision-language models in the 7 to 30 billion parameter range, served through an inference engine like vLLM, TensorRT-LLM or NVIDIA NIM, deliver document extraction quality close to leading cloud services while keeping data fully within the customer's infrastructure, which matters for legal, financial, healthcare and government documents subject to strict data residency or confidentiality rules. Hardware needs are modest for many document workloads: a single NVIDIA RTX PRO 6000 or H100 GPU comfortably handles moderate throughput, and Kubernetes with the NVIDIA GPU Operator adds scaling and monitoring as volume grows. The tradeoff against a cloud API is upfront infrastructure cost and the engineering effort to deploy, tune and maintain the pipeline, versus per-page cloud pricing and zero infrastructure ownership; for organizations processing large, sensitive document volumes, on-premise typically pays back its setup cost over time while also removing recurring compliance review of a third-party vendor. Nanobase AI, an NVIDIA Inception Program member, designs and installs on-premise document AI infrastructure so files never leave a customer's network.

What "on-premise" actually requires beyond a GPU

Running document AI on-premise is often framed as simply buying a GPU and downloading an open-weight model, but a production deployment needs the full pipeline, ingestion, OCR or vision-language model inference, classification, validation and integration, running inside the network boundary, not just the extraction model itself. The GPU serves the extraction and classification models, but the ingestion connectors, the review-queue interface and the integration into target systems all need to stay inside the same trust boundary, or the "on-premise" claim only covers part of the pipeline while documents still transit an external service somewhere else in the flow. Companies choosing on-premise for regulatory or confidentiality reasons should audit the entire data path, not just where the model runs.

Hardware tiers by throughput

A single RTX PRO 6000 or H100 comfortably handles moderate document throughput for most business workloads, and Kubernetes with the NVIDIA GPU Operator adds scaling and monitoring once volume grows beyond what a single GPU serves reliably.

Network isolation and air-gapped considerations

For the highest-sensitivity document types, legal privilege, classified government material, certain healthcare records, a genuinely air-gapped deployment with no outbound internet connectivity at all is sometimes required, which means the inference engine, model weights and any monitoring tooling all need to be installed and updated without relying on live external package repositories. Less extreme but still common is a network-isolated deployment that permits outbound connectivity for monitoring and updates but blocks any path for document content to leave the network, enforced through network segmentation and egress filtering rather than trust in application-level code alone. Either approach needs the inference stack, whether vLLM, TensorRT-LLM or NVIDIA NIM, deployed and validated within that boundary before any real document touches it.

Operational tradeoffs against a cloud API

The honest tradeoff is upfront infrastructure cost and the engineering effort to deploy, tune and maintain the pipeline, against a cloud API's per-page pricing and zero infrastructure ownership. For organizations processing large, sensitive document volumes over time, on-premise typically pays back its setup cost while also removing recurring compliance review of a third-party vendor's data handling practices, a real but often underweighted ongoing cost of a cloud approach in regulated industries. Organizations with lower volume or looser data residency requirements often find a cloud API more economical, since the fixed cost of on-premise infrastructure only pays off past a certain volume and sensitivity threshold.

Frequently asked questions

What GPU is enough for a first on-premise document AI deployment?

A single RTX PRO 6000 or H100 handles moderate throughput for most business document workloads comfortably; sizing should be based on measured pages-per-day and peak concurrency rather than assumed volume, since actual throughput needs are often lower than initial estimates suggest.

Does on-premise deployment mean no internet access at all?

Not necessarily; most on-premise document AI deployments use network isolation, blocking document content from leaving the network, rather than a fully air-gapped setup with zero outbound connectivity, which is reserved for the highest-sensitivity use cases like classified or privileged material.

How does Kubernetes fit into an on-premise document AI stack?

Kubernetes with the NVIDIA GPU Operator manages GPU scheduling, scaling and monitoring across the cluster as document volume grows, which matters once a single GPU can no longer handle peak throughput or when multiple document workloads need to share infrastructure efficiently.

How Nanobase AI helps

Nanobase AI, an NVIDIA Inception Program member, designs and installs on-premise document AI infrastructure, from GPU sizing through Kubernetes GPU Operator setup to network isolation, so files never leave a customer's network. See the on-premise LLM deployment guide and Kubernetes GPU Operator vs Slurm for infrastructure planning depth.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.