Neuramorphic offers the NeuraTensor SDK, a runtime that enables ultra‑fast, energy‑efficient inference of hybrid neuromorphic foundation models directly on commodity edge hardware such as NVIDIA Jetson AGX Orin. Its flagship NeuratronLLM‑Edge 4B model delivers sub‑25 ms latency with a 15‑50 W power envelope, providing on‑device AI for real‑time video, robotics, audio, and event‑camera workloads while guaranteeing zero data egress. The SDK includes custom CUDA kernels, drop‑in PyTorch compatibility, and an air‑gapped architecture, allowing developers to deploy privacy‑sensitive, low‑latency AI without deep hardware expertise.
Funding
Funding not disclosed
Founders
Product
Problem
Edge AI applications such as autonomous vehicles, industrial robots, and battery‑powered drones require inference that runs locally with millisecond latency, low power consumption, and strict data‑privacy, but existing AI frameworks rely on cloud services or heavyweight hardware, making them unsuitable for real‑time, on‑device deployment.
Solution
Neuramorphic provides the NeuraTensor SDK, a runtime that enables ultra‑fast, energy‑efficient inference of hybrid neuromorphic foundation models directly on commodity edge hardware. Their flagship NeuratronLLM‑Edge 4B model runs fully on a single NVIDIA Jetson AGX Orin, delivering sub‑25 ms inference while keeping all data on‑device. The SDK includes custom CUDA kernels that accelerate spiking neural network (SNN) and state‑space model (SSM) workloads by over 100× compared to standard frameworks. By eliminating cloud dependencies, the platform ensures zero data egress, meeting privacy and regulatory requirements for defense, healthcare, and regulated industrial use cases. Developers can integrate the runtime into existing PyTorch projects without deep expertise in computer architecture, allowing immediate deployment of real‑time video, robotics, audio, and event‑camera workloads.
Target Audience
Primary customers are developers and system integrators building real‑time AI solutions for autonomous vehicles, industrial robotics, edge IoT devices, and defense or healthcare applications that require on‑device inference and strict data sovereignty.
Features
- Custom CUDA kernels for NVIDIA Jetson AGX Orin and Ampere‑class GPUs delivering 111× performance gains on hybrid SNN + SSM workloads
- On‑device 4‑billion‑parameter NeuratronLLM‑Edge foundation model with 23 ms inference and zero outbound telemetry
- Sub‑25 ms inference latency and 15‑50 W power envelope suitable for battery‑powered and low‑power edge nodes
- Air‑gapped architecture guaranteeing that all data remains on the device, supporting privacy‑sensitive and regulated deployments
- Drop‑in compatibility with PyTorch, enabling developers to add edge inference to existing codebases with minimal changes
- Proven performance on real production workloads across domains such as security cameras, autonomous vehicles, quality inspection, and acoustic monitoring