One chip. Reasoning,
vision and adaptation.
Neuramorphic runs a 4B-parameter LLM, real-time perception and on-device learning on a single NVIDIA Jetson AGX Orin. No cloud round-trip. No data egress. Nothing to intercept.
Trusted by industry leaders
Three products. One piece of silicon.
A foundation model that reasons, a perception layer that sees, and a CUDA engine that runs both — concurrently, air-gapped, on a single Jetson AGX Orin.
NeuratronLLM-Edge 4B · Caroline
Hybrid SNN + SSM reasoning model, ~4B parameters, fully air-gapped. 9.74 tok/s mean throughput, on-device adaptation in 36 steps.
Neuravision
Real-time object detection (YOLOv8n) running concurrently with the LLM on the same chip. 24.83 FPS, p95 latency 36.7 ms.
NeuraTensor SDK
Custom fused CUDA kernels for hybrid SNN-SSM workloads on Ampere edge silicon (SM 8.7). 23 ms inference, 111× speedup, 4× memory reduction.
Two years. A new computing substrate.
NeuratronLLM-Edge 4B
First 4-billion-parameter Neuramorphic foundation model. Air-gap enforced by the kernel. The entire stack lands as one product.
Click or hover any milestone to jump
Models that run where the cloud can't.
Air-gapped, hybrid neuromorphic LLMs designed for the edge. Inference, adaptation and tokenization happen on a single device. No outbound calls.
NeuratronLLMEdge4B
Hybrid SNN + SSM kernel on top of a 4B base, running fully air-gapped on a single Jetson AGX Orin. Adapts on the device.
Parameters
~4B
Throughput
9.74tok/s
Vision
24.83FPS
Mean power
44.5W
NeuratronLLM-Edge · Generation 2
Larger context, multilingual, distilled vision-language
Micro-edge kernels
SNN-only inference for MCU-class targets
It doesn't just reason.
It sees, at the same time.
Neuravision is the real-time perception pipeline built into NeuratronLLM-Edge. It runs YOLOv8n object detection concurrently with full LLM inference — on the same Jetson AGX Orin, under the same 60 W envelope, with zero additional silicon.
For a drone, a camera or an inspection robot, that means the device can see and reason about what it sees in the same breath — no second accelerator, no round-trip to a server rack, no frame ever leaving the machine.
See the measured vision benchmarksMean FPS
24.83
YOLOv8n · 30 iterations
p95 latency
36.7ms
p99 37.62 ms
Runs alongside
9.74tok/s
full LLM inference, concurrent
Extra hardware
0
one Jetson AGX Orin, one envelope
Deployed in production today
One stack, mapped to where it earns its keep — sovereign defense, regulated industry, and anything that can't afford a round-trip to a data center.
Real-Time Video
Security cameras, quality inspection, autonomous vehicles
Robotics & IoT
Sensor fusion, motion planning, predictive maintenance
Sovereign & Defense
On-prem reasoning where data cannot leave the room
Industrial Inspection
Manufacturing lines, substations, remote pipelines
Battery-Powered
Drones, wearables, solar-powered edge nodes
Regulated On-Prem AI
Healthcare, finance, government — air-gapped by design
Edge AI without compromise
Most platforms make you trade performance for efficiency, or efficiency for privacy. Ours doesn't — because reasoning, vision and adaptation share the same chip and the same power budget.
Ultra-Fast
23 ms inference · NeuraTensor
Energy Efficient
44.5 W mean, under a 60 W cap
Private
Air-gapped, zero data egress
Production-Ready
Patent-pending, forensic-validated
Measured, not benchmarked
Direct measurement on a single Jetson AGX Orin 64 GB, power mode MAXN. No synthetic benchmarks — every number below is reproducible on the hardware we ship on.
LLM throughput
9.74tok/s
Caroline · FP16-native
Vision
24.83FPS
Neuravision · YOLOv8n
NeuraTensor speedup
111×
vs PyTorch reference
Memory footprint
4× lower
fused SNN-SSM kernels
Mean power
44.5W
peak 45.1 W · 60 W cap
Full methodology and raw logs on the Caroline and NeuraTensor spec pages.
Protected by Patent Pending
Our Neuratron™ neuromorphic edge inference architecture is the result of years of original research. A U.S. provisional patent application has been filed with the USPTO covering core innovations in spiking neural network deployment on resource-constrained silicon.
SNN Inference Engine
INT8 spiking autoencoder running on $2 Cortex-M33 silicon with 5.15 ms deterministic latency.
Quantization Pipeline
Power-3 weighted scoring + per-layer symmetric quantization that survives INT8 deployment.
Edge Deployment Stack
End-to-end toolchain from PyTorch training to ARM Cortex-M binary with embedded scoring weights.
Ready to deploy edge AI?
Foundation model, vision pipeline, and CUDA engine — evaluate them together on a single Jetson AGX Orin, or license the piece you need. Sovereign, regulated and defense deployments handled under NDA.