Neuromorphic computing
A class of computer architectures that emulate the structure and dynamics of biological neural systems — using event-driven (spiking) computation, in-memory operations and high parallelism.
Neuromorphic computing replaces the synchronous, clock-driven von Neumann pipeline with asynchronous, event-driven units that resemble neurons and synapses. The result is dramatically lower energy per inference, sub-millisecond latency for sparse workloads, and a natural fit for continuously streaming sensor data. Neuramorphic builds production-grade neuromorphic foundation models (NeuratronLLM-Edge) that run on commodity NVIDIA edge silicon while preserving the energy and latency benefits of the paradigm.
Spiking neural network (SNN)
Neural networks where information is encoded in discrete spikes over time rather than continuous activations, enabling sparse and temporally-precise computation.
SNNs operate over time: each artificial neuron integrates incoming spikes and fires when a threshold is crossed, producing extremely sparse activity. Hybrid neuromorphic models (such as NeuratronLLM-Edge 4B / Caroline) combine spiking dynamics with state-space layers to keep inference compatible with mainstream GPU hardware.
State-space model (SSM)
A sequence-modeling architecture that uses linear recurrent dynamics to model long-range dependencies with sub-quadratic compute, used as a backbone in modern long-context LLMs.
SSMs (Mamba, S4, RWKV, NeuratronLLM-Edge) replace the quadratic attention of transformers with parameterised state dynamics. This makes them ideal for streaming, on-device inference where memory and FLOPs are tightly bounded — exactly the constraint of an air-gapped Jetson AGX Orin deployment.
Edge AI
Artificial-intelligence inference (and increasingly training) that runs on the device generating the data — sensors, vehicles, robots, industrial controllers — rather than in centralised cloud data centres.
Edge AI eliminates cloud round-trips, removing both the latency and the data-egress risk that disqualify cloud LLMs from defense, healthcare and semiconductor environments. Neuramorphic ships the full pipeline — model, runtime (NeuraTensor), CUDA kernels and on-device adaptation — for true edge inference.
Air-gapped LLM
A large language model whose weights, prompts and activations never leave the local device — no internet connection is required at any point during inference or adaptation.
Air-gapped operation is the strongest possible privacy and security guarantee for AI: the model is physically incapable of leaking data because the network egress simply does not exist. NeuratronLLM-Edge 4B (Caroline) is built specifically for this regime: zero data egress, on-device adaptation, forensic logs.
Sovereign AI
AI capability that an organisation, agency or nation operates and controls end-to-end — model, infrastructure, data — without dependency on a foreign cloud provider.
Sovereign AI is increasingly mandated for defense, intelligence and critical-infrastructure deployments. Neuramorphic's air-gapped Jetson stack is a turn-key sovereign AI platform: customer keeps the model, the data, the silicon and the upgrade path.
On-device adaptation
The ability of a deployed model to fine-tune itself on local data — without uploading anything — typically using parameter-efficient methods such as LoRA or low-rank adapters.
NeuratronLLM-Edge 4B reaches operationally useful adaptation in 36 gradient steps directly on the Jetson, with no cloud connection. This converts a generic foundation model into a customer-specific expert in minutes, while keeping every byte of training data on the device.
Continual learning
A learning regime where a model keeps improving from a stream of new data without catastrophically forgetting what it already knew.
Continual learning is the natural complement of on-device adaptation: the model evolves in production, in the customer's environment, against the customer's data distribution. Hybrid neuromorphic architectures are particularly resistant to catastrophic forgetting because of their sparse, time-distributed representations.
NeuratronLLM-Edge 4B (Caroline)
Neuramorphic's flagship 4B-class hybrid neuromorphic foundation model. Runs fully air-gapped on a single NVIDIA Jetson AGX Orin.
Forensic-validated metrics: 9.74 tok/s text, 24.83 FPS concurrent vision (YOLOv8n), 44.5 W mean power, on-device adaptation in 36 steps, zero data egress. Built for defense, semiconductors, healthcare and regulated edge deployments.
NeuraTensor
Neuramorphic's neuromorphic runtime / inference engine — custom CUDA kernels, scheduler and memory layout tuned for hybrid-neuromorphic workloads on Jetson-class hardware.
NeuraTensor is what makes Caroline possible: hand-written CUDA kernels for the spiking and SSM blocks, fused operators, 8-bit weight paths and a scheduler that overlaps vision and LLM inference on the same GPU. Available as a standalone runtime for partners shipping their own neuromorphic models on edge hardware.
Jetson AGX Orin
NVIDIA's flagship edge-AI module: ~275 TOPS, 64 GB unified memory, 60 W envelope. The reference platform for production neuromorphic inference at Neuramorphic.
The 64 GB Orin gives a hybrid neuromorphic 4B model enough room for live LLM inference, simultaneous vision (YOLOv8n) and on-device adaptation buffers — all without leaving the device. Ideal hardware for sovereign, air-gapped deployments.