Pick models without being capped by onboard GPU memory.
For policy or vision models the robot cannot host, we evaluate server-grade GPU inference to move the extra compute load off the robot.
TRAIN. DEPLOY. OPERATE.
From trained models to deployed robot skills.
We connect model training, cloud inference and on-site robot execution. Compute and deployment location are chosen per task, with data, model versions and real-robot evaluation in one traceable delivery process.
LOW-LATENCY CLOUD INFERENCE FOR ROBOTICS
Low-latency cloud inference
for robot models
Cloud skill deployment and robot integration
Deploy trained robot models to the cloud and jointly optimize the model, GPU kernels, network and nearby placement. An on-site gateway connects the robot, turning model inference into a callable, measurable and traceable skill service. Cloud and edge inference coexist; not every robot belongs in the cloud.
Project-based adaptation and validation · Not an off-the-shelf generic robot skill library
Low latency is the optimization target; actual performance depends on tests with the specific model, device and network.
DEPLOY A SKILL, NOT JUST A MODEL.Deployment flow
Inference serviceModel + adaptation config
Versioned skill
Observations ↑Results ↓
RobotOn-site gateway + local control
Emergency stop and low-level control never wait for the cloud.
Compute organized around the experiment.
Multiple compatible devices can share a service, reducing duplicated environments and idle capacity. Whether it saves money depends on GPU, network, storage and operations costs together.
You update skill versions, not every device.
Models, adaptation configs and evaluation records are managed centrally, then validated and released device by device in batches; if a regression appears, roll back to a validated version.
CLOUD INTELLIGENCE. LOCAL CONTROL.
Where compute runs depends on the task.
Cloud and edge inference coexist; this is not about moving all control to the cloud. Choose the deployment location by task latency, network conditions, data rights and total cost.
Nearby region · Cloud GPU
Models in the cloud, control on site.
For research and pilots with stable networks where authorized data may leave the site. Deployment location is evaluated by the robot's region; cross-region links are never assumed to be low-latency.
- Main value
- Avoids fitting every robot with large-memory compute and makes model environments and versions easy to share.
- Validate before deployment
- Validate end-to-end tail latency, network jitter, concurrency queuing and data transfer cost; keep the necessary local compute.
Cloud / server inference
Task understanding, policy inference, candidate action chunks
Authorized observations up / time-limited results down
On-site gateway
Timestamps, state alignment, action mapping and validity checks
Only actions that pass constraint checks are executed
Robot local control
Servo control, limits, emergency stop and validated fault handling
Execution results and exceptions → authorized logs → evaluation / re-capture
ENGINEER THE WHOLE PATH
From a single model to a deployment you can sign off on.
Choose the service scope per project; supported models, robot models and deliverable features are confirmed after pilot validation.
Not just cheap compute: the whole inference chain, optimized.
Model
Evaluate quantization, inference steps and action-chunk strategy, validating the trade-off between task performance and response time.
GPU kernels
Evaluate operator, compiler and memory-access optimizations per model and hardware; numerics and policy behavior are re-validated after acceleration.
Network
Optimize observation encoding, transport and connection management to control bandwidth, jitter and stale data, looking beyond average latency.
Nearby placement
Choose nodes by robot region and site conditions, combined with warm-up, concurrency scheduling or on-site private compute.
Joint optimization targets verifiable end-to-end performance, not single benchmark scores. Cloud, edge and local options are compared on the same task baseline, with no assumption that the cloud is better.
Model and skill packaging
Weights, preprocessing, I/O conventions, robot adaptation and runtime conditions organized into one traceable version.
Delivers: skill manifest / adaptation configInference service deployment
Choose cloud or private GPUs per task, evaluate model optimization, warm-up and concurrency isolation, and set up monitored inference endpoints.
Delivers: deployment config / performance reportRobot-side integration
Align cameras, state, coordinate frames and action space, connect to the local gateway, and validate action validity windows, constraint checks and timeout behavior.
Delivers: interface adaptation / fault testingEvaluation, release and feedback
Offline replay and shadow inference first, then supervised real-robot trials; failures are logged, releases staged, and re-captured data linked to model versions.
Delivers: acceptance report / rollback procedureA skill is more than a weights file.
Grasping and sorting, dual-arm coordination or mobile inspection all need validation on specific devices and tasks. Reusing across robots means re-confirming action space, end effector, camera layout and calibration.
- Inputs: cameras, robot state, task goal
- Outputs: subgoals or action chunks, timestamps and validity windows
- Adaptation: coordinate frames, control interfaces, action constraints
- Version: weights, environment, operating conditions and evaluation records
What if the cloud responds slowly or the network drops?
Heartbeats, observation freshness and action validity are checked continuously; on timeout or state mismatch, stale actions are rejected. Depending on the risk, the robot enters a controlled local stop, a safe hold or a validated fallback, with human takeover preserved. When the network returns, state is re-synced and old commands are never replayed; high-frequency contact control never depends on an internet round trip.
How do you know a deployment actually works?
Measure end-to-end latency from observation to execution, not just GPU inference time; record P50 / P95 / P99, jitter, packet loss, concurrency queues and resource start-up time. Then check task success rate, human intervention, timeout handling, version rollback and total operating cost. No fixed millisecond figure or generic success rate is assumed.
How is this different from Router, GPU Cloud and the Data Engine?
Router provides a general model API entry point; GPU Cloud provides compute. Robot skill deployment also needs an independent model runtime, device adaptation and local control path. Authorized execution logs can be linked to data versions and re-capture tasks. The general model API and robot control interfaces are built separately and enabled per project.
Have a model or a robot? Start with a deployment assessment for one task.Discuss device and model adaptation
ROUTER / MODELS & COMPUTE
RouterConnect to models and to compute.
From a unified model API to the GPU Cloud compute environment, choose the right service for data processing, experiment development and model training.
01 / API ROUTER
Call models without re-integrating each one.
Access enabled models through one gateway and manage API keys, model permissions and spending limits; view call logs, token usage and billing in the console.
Apps / data toolsAPI RouterModel services
Open the Router console 02 / GPU CLOUD
Run experiments on the right compute.
Open GPU Cloud for compute and algorithm services for embodied data processing, VLA and world-model training. Confirm resource needs by model size, GPU memory and runtime environment.
Training / data processingGPU CloudCompute environment
View GPU compute services Model API calls and GPU compute resources are managed separately and are not robot control interfaces. Available models, resources, access and billing are defined by each platform; project data may only leave your environment with authorization.
THREE DIFFERENT WORKLOADS
Configure three kinds of compute separately.
Local capture workstation
Capture and data processing
Serves cameras, sensors, playback and preprocessing, with a focus on interface bandwidth, storage write speed, time sync and continuous operation.
Cloud or your own GPU cluster
Model training and tuning
GPU memory and count are chosen by model size, sequence length, visual inputs, batch size and precision; resources are estimated after a small trial run.
Cloud inference + on-site control / local deployment
Online robot inference
Choose cloud, on-site or local inference based on latency, network, power and data rights. High-frequency control and safety handling stay on the robot. Models still need interface and runtime adaptation.
MODELS ARE NOT INTERCHANGEABLE
A model API is not a robot control API.
Understanding and data-processing models
Vision or language APIs can support task descriptions, candidate labels, retrieval and quality checks. Their outputs need review and are never treated as ground-truth actions.
Robot policy models
Camera inputs, state fields, action space, end effector and control frequency must be defined. Correct offline output does not mean safe execution on a real robot.
REPRODUCIBLE BY DESIGN
Training services that deliver traceable experiments.
Experiment configuration
Data version, splits, preprocessing, hyperparameters, random seeds and environment dependencies.
Model artifacts
Weights, configs, model licenses and third-party dependency lists included in the delivery scope.
Evaluation report
Fixed tasks and trial conditions, recording success rate, human intervention, time taken and failure categories.
Resource records
GPU type, runtime, storage and API usage recorded by resource category.
START WITH YOUR TASK
Tell us about your research task.
From device selection to data and training workflows, we'll help you find the right place to start.