NeuroTensor Labs builds proprietary Vision-Language-Action (VLA) foundation models that deliver sub-8ms spatial geometry and obstacle reasoning for autonomous mobile robots, aerial drones, and industrial manufacturing lines.
Experience how NeuroTensor's compiled spatial foundation model processes high-frequency sensor streams, zero-shot bounding boxes, and metric depth in sub-6ms cycles.
From multi-camera spatial reasoning to ultra-compact robotic execution, our unified architecture powers autonomous agents across cloud, on-prem clusters, and embedded edge compute.
Our multimodal Vision-Language-Action foundation model pre-trained on 400B spatial tokens. Unifies dense 3D geometry prediction, spatial semantic affordances, and zero-shot trajectory generation into a single end-to-end transformer.
Proprietary compiler toolchain that compiles large spatial neural networks into hyper-optimized NVIDIA TensorRT execution engines, generating sub-8ms deterministic runtimes for Jetson AGX Orin & Thor.
Synthetic data simulation pipeline built upon NVIDIA Isaac Sim and Omniverse USD assets. Synthesizes millions of edge-case physical failure scenarios, lighting shifts, and extreme physical dynamics to pre-train robust edge agents.
Pre-training large spatial VLA models requires massive distributed GPU clusters. Through the NVIDIA Inception Program, NeuroTensor Labs is applying for DGX Cloud access and compute credits to train our next-generation 70B spatial foundation model across multi-node H100 SXM5 clusters.
Dynamic estimation based on Chinchilla compute scaling laws
Comprehensive presentation covering the market problem, TAM ($48.5B), proprietary CUDA/TensorRT architecture, traction benchmarks, and NVIDIA compute partnership goals.
Our team combines cutting-edge deep learning research from Stanford AI Lab and CMU with industrial scale infrastructure engineering.
Co-Founder & CEO • ex-Stanford AI Lab
PhD in Computer Vision & Spatial Robotics from Stanford University. Former Senior Perception Scientist with 12+ peer-reviewed publications across CVPR, ICCV, and NeurIPS. Pioneer in low-latency transformer inference for physical agents.
Co-Founder & CTO • PhD CMU
PhD in High-Performance GPU Computing from Carnegie Mellon University. Former NVIDIA Autonomous Machines Research Intern. Specialist in CUDA kernel micro-optimizations, FP8 quantization, and TensorRT compilation engines.
VP Engineering • ex-AWS HPC Architect
10+ years scaling high-performance compute clusters. Architected 1024-GPU InfiniBand clusters for foundational LLM training. Oversees Triton cluster deployments, model checkpoint synchronization, and hardware telemetry.
"By fusing spatial geometry representations directly into TensorRT-accelerated action layers, NeuroTensor Labs is solving the fundamental millisecond barrier that has held back autonomous robots in unstructured dynamic environments."
Our foundation architecture is built upon rigorous mathematical proofs and published spatial geometry benchmarks.
Dr. Aryan Mehta, Dr. Elena Rostova, Marcus Vance, Prof. Hiroshi Tanaka
Dr. Elena Rostova, Dr. Aryan Mehta, NVIDIA Collaborators
Join our closed developer pilot for robotics OEMs, autonomous vehicle labs, and industrial automation teams. Pre-compiled TensorRT engines delivered directly to your engineering team.
