Accelerated computing
Physical AI, at the plant edge
A tint station reads a draw-down in seconds and a filling line moves hundreds of pails an hour, so perception has to be local and fast. Tinteon runs GPU inference at the plant edge and keeps training, simulation and optimization in the cloud, in a private cloud or on-prem. Items marked planned are on the roadmap, not running.
Jetson Orin / Thor · DeepStream · TensorRT · Holoscan
Edge perception
8 to 24 camera and sensor streams per line through DeepStream pipelines and TensorRT color, gloss, hiding-power, contamination and fill models; Holoscan synchronizes spectrophotometers, line-scan cameras, inline rheology, mill power and fill-weight streams at the plant edge. Design target: 50 to 150 ms per frame.
Triton · NIM
Model serving
Vision, spectral, time-series and coatings-knowledge models behind one plant router, with version routing by product family, colorant system and line. Design target: sub-second recommendations for operators, sub-250 ms holds for filling and inspection.
DGX / HGX · NeMo
Training (planned)
Spectral color-match, dispersion and fineness, rheology and contamination models fine-tuned on batch telemetry, images and supervised color-matcher corrections; NeMo-based coatings reasoning. Design target: 2,000 to 5,000 GPU-hours per major model family.
Omniverse · Modulus · OVX
Digital twin (planned)
The Coatwin as-made batch twin: GPU-accelerated spectral color-match prediction and dispersion and rheology simulation run before the batch, for color and grade changes and batch planning when a trial is expensive. Design target: 10,000 to 100,000 scenarios per design partner.
Cosmos · Omniverse Replicator
Synthetic data (planned)
Rare off-color, seed, contamination, foam, label, hiding-power and fill-fault cases generated with domain randomization, marked synthetic and validated against real plant data before use.
cuOpt
Optimization
Color-family batching, wash and changeover minimization, solvent and energy allocation and filling-line takt, recomputed on the GPU daily and intra-shift when the plant is disrupted.
RAPIDS
Data engineering
Batch-genealogy, quality, equipment and rework telemetry ETL for feature generation, color correlation and cross-plant benchmarks on the GPU.
Isaac · Jetson
Robotic handling
Drum, pail, tote and pallet handling, with perception and motion policies built and validated in simulation before they move a container.