Not all data can leave the building. In defense, healthcare, critical infrastructure, and government environments, sending images to a cloud API is simply not an option. When your visual intelligence system needs to run entirely on your own hardware, the engineering challenges multiply, but the solutions are very much within reach.
When LLMs become the bottleneck
We see a common pattern with our clients: they start using large language models with vision capabilities for tasks like reading documents, detecting objects, or counting items in images. It works. The results are decent. But at some point, the cracks appear. Processing gets slow. Costs spiral. Latency becomes unacceptable for real-time workflows. And accuracy on specialized tasks plateaus because the model was never designed for that specific job.
The AI Factory helps organizations move from general-purpose LLM-based vision to dedicated computer vision models that run entirely on your own servers. The result is almost always the same: faster, more accurate, and dramatically cheaper. A custom-trained OCR model outperforms GPT-4 Vision on your specific document types. A fine-tuned object detector runs in milliseconds instead of seconds. A counting model trained on your actual data delivers precision that a general model never will.
Computer vision works best when you make it specific. OCR for your exact document formats. Object detection tuned for your factory floor. Counting models calibrated for your inventory. Generic models give you generic results. Dedicated models give you production-grade performance.
Why on-premises matters
There are four main reasons organizations choose on-premises deployment for their vision workloads:
Regulatory requirements. Real-time inspection systems in manufacturing lines, security surveillance, or autonomous vehicles cannot tolerate the round-trip latency of a cloud API call.
Latency-critical applications. When millisecond response times matter, local inference on dedicated hardware eliminates network variability entirely.
Connectivity constraints. Systems deployed in remote locations like offshore platforms, rural infrastructure, or mobile units may have intermittent or no internet connectivity.
Cost at scale. Processing thousands of video frames per second through a cloud API quickly becomes cost-prohibitive.
The architecture
A production-grade on-premises CV system has four layers:
1. Data pipeline
Camera feeds, uploaded images, or batch imports flow into a standardized ingestion pipeline. This handles format conversion, resolution normalization, and metadata extraction.
2. Model inference
Optimized models run on local GPU hardware, typically NVIDIA GPUs with TensorRT or ONNX Runtime for maximum throughput.
3. Post-processing & business logic
Raw model outputs pass through business logic that determines what constitutes an actionable event. This layer handles thresholding, deduplication, temporal smoothing, and alert generation.
4. Monitoring & retraining
Automated drift detection tracks model performance over time and collects edge cases for periodic retraining, all without data leaving the premises.
Hardware considerations
The hardware choice depends entirely on throughput requirements. For modest workloads, a single workstation with an NVIDIA RTX GPU is sufficient. For large-scale deployments, we spec multi-GPU servers or clusters.
Edge deployment is another option: NVIDIA Jetson devices can run lightweight models directly at the camera location, only transmitting results (not raw video) to a central server.
From our experience
We have deployed vision systems that process video at the edge, running inference directly on camera hardware and only transmitting results (not raw video) to a central server.
Our on-premises solutions consistently match or exceed the capabilities of cloud-based alternatives, with complete data control and zero dependency on external services.
Need a computer vision system that runs on your own infrastructure?
Get expert advice