NodeWeaver hosts inference, vision, and agentic workloads on the same infrastructure that powers your business — with GPU passthrough, real-time scheduling, and offline resilience. Ship models to the floor without redesigning your stack.
The patterns we see, again and again, in every customer environment we walk into.
Camera streams, sensor data, and control loops can't round-trip to a hyperscale region. Latency, bandwidth, and data residency all conspire against it.
Per-app inference boxes don't share GPU, don't share storage, don't share networking. You end up with one stranded GPU per camera array.
Your training pipeline pushes models to a registry. Then what? Pushing weights to a thousand sites, with rollback, canary, and observability, is a separate problem entirely.
Capability that matters most for this vertical, packaged with reference architectures and pre-validated hardware.
Dedicate GPUs to inference workloads with direct passthrough and real-time scheduling guarantees. Any PCI device — NVIDIA or Intel GPUs, TPUs, FPGAs, capture cards — passed through at full performance.
Run vision, ASR, OCR, and LLM inference where the data lives. Nothing leaves the site unless you explicitly send it.
Push new model versions through canary, rollback, and update windows across thousands of sites — using the same controls as your OS updates.
Soft real-time scheduler keeps inference loops on-budget even when the cluster is loaded — and gives you observability for tail latency, not just averages.
NodeWeaver's distributed storage keeps accelerators fed — near-perfect utilization even at ten GPUs on a single node.
MLPerf storage benchmark · single node · Unet3D
We are able to reduce the customer's cost of acquisition and deployment by 80%, while significantly increasing reliability, uptime, and giving them complete supply chain flexibility for server hardware going forward.
Thirty minutes. We walk through your topology, your workloads, your hardware. You leave with a reference architecture and a number.