Industry: distributed inference for Physical AISee the runtime
NYRMap one robot fleet

Physical AI infrastructure

Put robot intelligence closer to the work.

Run your models near the machines, sensors, and environments they understand. Keep inference responsive through changing links, protect high-volume sensor context, and adapt one Physical AI product to every fleet and site.

Map one robot fleet Explore use cases

For robotics, industrial inspection, autonomous machines, embodied agents, and fleet software teams.

PHYSICAL AI DEPLOYMENT SITE ONLINE
Isometric NYR Physical AI runtime at a robot siteCamera, lidar, and machine-state cubes send context across an isometric customer-site plane to a raised NYR runtime. The model returns an inference result to the customer-owned autonomy stack, while a separate policy-controlled path can synchronize with the vendor cloud.NYR RUNTIME · ROBOT 07CAMERAsceneLIDARrangeMACHINEstateAPPROVED CAPACITYNYR RUNTIMEPHYSICAL AIMODELAUTONOMYCUSTOMER STACKcustomer-owned · authoritativeVENDOR SYNCwhen availableSimplified NYR Physical AI runtimeSensors send context to an NYR-managed local model, which returns an inference result to the customer-owned autonomy stack. Optional vendor synchronization follows a separate policy-controlled path.APPROVED ROBOT SITESENSORSCAMERALIDARSTATECONTEXTNYR LOCAL RUNTIMEPHYSICAL AI MODELPLACED · PROFILED · OBSERVEDRESULTAUTONOMY STACKCUSTOMER-OWNED · AUTHORITATIVEVENDOR SYNC
live sensor path local inference policy-based sync
01Shorter inference pathReason closer to live sensor context
02Resilient operationKeep an eligible local path available
03Private sensor dataProcess high-volume inputs in bounds
04Fleet-specific fitTune by machine, site, and task

Where placement changes performance

The physical world keeps moving.
Inference has to keep up.

A robot’s model sits inside a live operating system of sensors, machines, people, networks, and safety controls. NYR gives the AI vendor one way to place and operate that model across them.

01Action inference

Keep the next decision close to the moving world.

Place VLA, planning, and reasoning workloads near the machine so a remote round trip does not become the default path between observation and action.

02Inspection intelligence

Interpret high-volume sensor context on site.

Run vision, thermal, acoustic, and anomaly models where data is produced, then send the product the useful result instead of every raw stream.

03Fleet resilience

Keep useful intelligence available when links change.

Use health-aware placement across approved onboard and site capacity, with workload policy that accounts for disconnected and degraded operating conditions.

04Site adaptation

Give one robot brain a local operating profile.

Tune model version, environment context, sensor mix, operating policy, and vocabulary by facility without rebuilding the AI product for every deployment.

BRING A REAL MACHINEMap one sensor-to-model path.

We’ll trace the sensors, model chain, compute envelope, site boundary, and fallback behavior.

Map one robot fleet

The Physical AI deployment path

Keep your robot brain.
Give it a production runtime.

NYR operates underneath the autonomy product. Your team owns the models, controls, and experience; NYR handles compatible placement, site profiles, health, and the path to approved inference capacity.

AI VENDORONE PHYSICAL AI PRODUCTmodels · policies · releases
ROBOTonboard inferencemachine context
SITEnearby accelerationshared sensor context
REGIONapproved capacityfleet services
01

Describe the operating envelope

Define the workload, machine class, accelerator, sensor inputs, response path, privacy boundary, and connectivity policy.

02

Place approved models

NYR schedules the AI vendor’s versioned model on compatible onboard, site, or regional capacity close to the machines it serves.

03

Adapt by fleet and site

Apply local model profiles, environment context, thresholds, integrations, and operating policy without forking the core product.

04

Operate through change

Observe health and apply the customer-defined release and fallback policy across approved capacity when conditions change.

Why distributed Physical AI

Stop treating every machine
like a cloud endpoint.

REMOTE-ONLY INFERENCE

The model is far from the world it operates in.

  • PATHEvery decision depends on a changing external link
  • DATARaw, high-volume sensor context travels farther
  • FLEETOne deployment assumption meets different hardware
  • SITELocal conditions become exceptions in product code

More network dependency. More data movement. More rollout-specific work.

WITH NYRDISTRIBUTED RUNTIME

The model runs on the eligible capacity closest to the task.

  • PATHLocal placement shortens the route to machine context
  • DATASensor inputs can be processed inside approved boundaries
  • FLEETWorkloads target compatible robot, site, or regional capacity
  • SITEProfiles tune behavior without cloning the product

One product. A practical inference path for every machine and site.

One brain, different embodiments

Reuse the intelligence.
Adapt the operating profile.

A mobile inspector, manipulator, and autonomous vehicle can share your core intelligence without sharing the same sensors, accelerator, task policy, or environment map.

MACHINE 01Mobile inspectionthermal · acoustic · mapped facility
MACHINE 02Manipulationvision · force · task vocabulary
MACHINE 03Outdoor autonomylidar · rough terrain · degraded link
NYR PROFILE SETONE RELEASE · 3 TARGETS

Place each workload deliberately

On the robot, at the site,
or in the right region.

01

Onboard

Keep machine-sensitive inference with the robot when the workload fits approved onboard compute.

best for · immediate machine context
02

Site edge

Share stronger nearby acceleration across machines while keeping sensor processing inside the facility.

best for · multi-robot operations
03

Regional capacity

Place eligible fleet services in an approved region when broader context or larger compute is required.

best for · fleet-wide reasoning
START WITH ONE SITEChoose where the first workload should run.

Onboard, at the site, or in an approved region. We’ll map the first production placement.

Plan the first fleet rollout

Physical AI infrastructure FAQ

What robotics teams ask before a distributed rollout.

01Does NYR replace the robot autonomy stack or control system?

No. NYR is the distributed orchestration and inference layer beneath the Physical AI product. The vendor keeps its models, autonomy software, control system, safety architecture, and customer experience.

02Which Physical AI workloads fit a distributed runtime?

Examples include VLA and planning inference, vision and anomaly detection, multimodal inspection, local assistants, fleet intelligence, and other workloads that benefit from proximity to machines and sensor context. Hard real-time safety loops remain in the dedicated control and safety stack.

03Can NYR support different hardware across a fleet?

NYR can model hardware compatibility and placement policy so each approved workload targets suitable capacity. A rollout can select different model builds or profiles for different accelerators, embodiments, and sites.

04What happens when a site loses its external connection?

Workloads placed on approved local capacity can continue using their local serving path. Sync, updates, and remote operations can follow the product’s policy when connectivity returns; exact behavior depends on the application’s architecture and safety requirements.

05Does local inference mean every model must run onboard?

No. The right placement may be onboard a machine, on a nearby site server, or in an approved regional zone. NYR is designed to choose from the eligible capacity rather than force one location for every workload.

Bring a real machine and site

Where should your Physical AI model run?

Bring the model, machine classes, accelerators, sensor paths, site constraints, and connectivity policy. We’ll map the first production placement across your approved capacity.

Map one robot fleet