Vantar AI

Infrastructure for
physical AI.

Software is eating the physical world. Robots, edge devices, neuromorphic chips - they all need better developer tools. Vantar builds the missing infrastructure layer: from robotics CLIs to neuromorphic compilers to edge deployment.

Why Vantar

Physical AI is where software meets the real world. The tooling hasn't kept up. We are fixing that.

Robotics tooling is broken

Robotics developers spend 40-50% of their time on environment setup, dependency management, and build system wrestling. Torq replaces the entire fragmented toolchain with one CLI - init, build, sim, deploy.

Neuromorphic has no DevTools

27+ SNN frameworks exist for simulation and training. Zero tools exist for experiment tracking on neuromorphic hardware. Nuro fills the gap - record, track, visualize, and deploy across any chip.

Physical AI needs a platform

Robots, edge devices, neuromorphic chips - they all need better developer tools. From training to edge deployment, one company, one ecosystem. That is what we are building.

Torq

The fast robotics development toolkit. One CLI replaces 15 minutes of setup hell. Written in Rust.

Terminal

$ torq init my-robot --template mobile
  Created my-robot/ with Nav2 mobile template
  ROS2 Jazzy workspace ready

$ torq build
  Cached build — 3.2s (was 4 min with colcon)
  0 warnings, 0 errors

$ torq sim
  Launching MuJoCo — warehouse.xml
  Robot spawned. Ctrl+C to stop.

$ torq deploy
  Deploying to jetson-orin@192.168.1.50
  Syncing build artifacts... done
  Restarting robot.service... running

75x

Faster builds

Cached Rust pipeline

5

Templates

minimal, mobile, arm, drone, humanoid

3

Simulators

MuJoCo, Gazebo, Isaac

1

CLI

init, build, sim, deploy

View on GitHub

Nuro SDK

The universal SNN compiler. Train on GPU with surrogate gradients. Deploy to neuromorphic silicon. Zero code changes.

import nuro

# Define a spiking neural network
graph = nuro.Graph()
graph.add(nuro.neurons.LIF(128, tau=20e-3))
graph.add(nuro.neurons.LIF(10, tau=20e-3))

# Train on GPU — surrogate gradients
model = nuro.compile(graph, target="gpu", requires_grad=True)
model.fit(train_data, epochs=50)

# Deploy to neuromorphic silicon — same model
loihi = nuro.compile(graph, target="loihi")   # auto-quantizes
spinn = nuro.compile(graph, target="spinnaker")
akida = nuro.compile(graph, target="akida")

# Track experiments
exp = nuro.experiment("snn_baseline", project="edge_vision")
exp.set_hardware("loihi-2", board_id="ncl_04")
exp.add_recording("spikes", loihi.record())
exp.log_metrics({"accuracy": 0.94, "power": "0.8mW"})
exp.save("./experiments")

5

Backends

GPU, Loihi, SpiNNaker, Akida, Samna

4

Neuron models

LIF, Izhikevich, AdEx, IF

227

Tests passing

Full stack coverage

0

Code changes

Same model, any hardware

View on GitHub

Roadmap

Four layers. CLI to compiler to cloud to silicon. Each product is useful standalone - together they are the full stack for physical AI.

Now

Torq CLI

Torq v0.1 — Shipped

Open-source Rust CLI for robotics. Project scaffolding with 5 templates, cached builds, simulator orchestration (MuJoCo, Gazebo, Isaac), and SSH deployment to robot hardware. One tool replaces the entire ROS2 setup workflow.

RustROS2MuJoCoApache 2.0
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Now

Nuro SDK + DevTools

Nuro v0.8 — Shipped

Universal SNN compiler with experiment tracking. Train on GPU with surrogate gradients, deploy to Intel Loihi 2, SpiNNaker 2, or BrainChip Akida. Hardware-agnostic recording, experiment tracking, and visualization built in.

PythonNIRANN-to-SNNApache 2.0
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Next

Vantar Cloud

Cloud v0.9 — In Progress

Push experiments to the cloud. Compare runs across hardware platforms. Share recordings with collaborators. Remote access to neuromorphic chips and robot simulators you don't own yet.

experiment sharingremote hardwarefree tier
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Future

Vantar Dev Kit

Exploring

A complete edge AI module for event-based vision. Hybrid event camera paired with a neuromorphic processor. Nuro pre-installed. One device from sensor to inference.

event cameraedge modulesub-1mW
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Built For

Robotics Engineers

Build and deploy robots faster with Torq. One CLI replaces fragmented ROS2 tooling - scaffold projects, cached builds, simulator orchestration, and hardware deployment over SSH. Stop wrestling with setup.

Neuromorphic Researchers

Track experiments and deploy SNNs with Nuro. Record spike trains from any hardware, log parameters and metrics, visualize with one line, and compile to Loihi, SpiNNaker, or Akida with zero code changes.

Edge AI Teams

Full stack from training to silicon to device. Train on GPU, deploy to neuromorphic chips, run on edge hardware. One ecosystem covers the entire pipeline from research to production.

Join the ecosystem.

Open source. Apache 2.0. From robotics CLIs to neuromorphic compilers to edge deployment - one platform for physical AI.

Early Access - Cloud & Hardware