Vision and quality-control inference has to keep pace with the line — which means the AI compute belongs next to the cameras. We place GPUs on-prem and at the edge, size them to the production line's cycle-time constraint, and keep central capacity for training while the trained models run on the floor.
Manufacturing AI lives on the floor, where the constraint is unforgiving: a quality-control vision model that gates a part has to return its verdict inside the cycle time, at the line's throughput, every time. The line does not wait for a round trip to a remote data center, and inference that stalls when the network hiccups is worse than no inference at all when a stoppage costs real money per minute. That points the compute toward the cameras — edge GPUs at the cell, or a plant-floor server in the building.
We treat training and serving as different problems. Central GPU capacity trains and retrains the models; the trained models are pushed out to edge hardware that serves them in real time on the line. We size the edge from the cycle-time constraint backward — frame rate and model set the throughput, cycle time sets the latency budget — and use quantization and optimized runtimes so a smaller, cheaper, often fanless edge unit can still hit the rate, rather than overbuilding every cell.
GPU capacity placed where the line runs and sized to the cycle-time constraint it has to hold.
Value concentrates wherever inference has to hold the line and the compute has to live near the work:
Quality-control vision has to keep pace with the line, and the line does not wait for a round trip to a remote data center. Inference that gates a part has to return inside the cycle time, so the compute belongs next to the cameras — on edge GPUs at the cell or a plant-floor server in the building. Edge placement also keeps inference running when the network does not, which matters when a stoppage costs real money per minute. We use central GPU capacity for training and retraining, and push the trained models out to edge hardware for serving.
The line cycle time is the hard constraint — every inference has to complete within the window the part is in front of the camera, at the line's throughput. We size from that: the frame rate and model set the required throughput, the cycle time sets the latency budget, and we choose edge GPU class and count to hold both with margin. Where the budget is tight we apply quantization and optimized runtimes so a smaller, cheaper, fanless edge unit can still hit the cycle rather than overbuilding every cell.
Bring your line cycle times, your vision models, and your cell count. In thirty minutes we will show an edge-and-on-prem GPU plan sized to hold the cycle without overbuilding the floor. Response inside 24 hours.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: