NeuroTensor Labs
Real-Time Spatial World Models for 100 Million Autonomous Robots
Developing sub-8ms Vision-Language-Action (VLA) foundation models accelerated natively on NVIDIA TensorRT, CUDA-X, and Isaac Sim for autonomous mobile robots and physical edge systems.
The 50ms Latency Wall in Physical Robotics
Current multimodal models (LLaVA, RT-2, open-source VLMs) are designed for cloud chat and operate at 200ms–1500ms cycle times. In the physical world, an AMR moving at 2 m/s travels 1 meter before perceiving a moving human obstacle.
Unacceptable Latency
Standard PyTorch models take >50ms on edge chips, causing jitter, frequent emergency stops, and robotic navigation failure.
VRAM Memory Bloat
Standard 7B models consume 14GB+ VRAM, leaving zero compute headroom for path planning and motor control on embedded edge boards.
Sim-to-Real Gap
Models trained only on 2D internet photos fail catastrophically when confronted with changing industrial lighting and 3D depth variations.
NeuroVoxel: Native Spatial VLA Foundation Model
A breakthrough multimodal transformer that treats spatial geometry as a primary modality alongside RGB vision, executing in sub-8ms directly on NVIDIA Jetson and Thor edge hardware.
Unified Geometry-Action Tokens
Rather than decoupling perception, mapping, and motion planning into 3 separate pipelines, NeuroVoxel processes RGB-D sensor streams into unified 6-DoF trajectory primitives within a single forward pass.
Hardware-Aware TensorRT Compilation
Our compiler directly generates TensorRT 10 execution engines using FP8 SmoothQuant and FlashAttention-3 kernels, compressing memory footprints down to under 2GB VRAM.
Built on NVIDIA Accelerated Computing
Every layer of the NeuroTensor pipeline leverages NVIDIA software libraries and hardware primitives for maximum TFLOPS utilization.
TensorRT 10.0
Automatic kernel fusion & FP8 execution graph optimization.
Isaac Sim & USD
Photorealistic synthetic digital twin training data generation.
Triton Server
Dynamic batching and multi-tenant GPU deployment across edge fleets.
CUDA 12.6 Kernels
Hand-tuned warp-level reduction for 3D point cloud voxelization.
$48.5 Billion Total Addressable Market by 2030
The convergence of generative AI and physical machines is creating an exponential demand for low-latency foundation models that can be embedded into mass-market robots.
Global intelligent robotics software, spatial perception sensors, and autonomous machine intelligence by 2030 (34% CAGR).
Autonomous mobile robots (AMRs) in logistics, factory automation, drone delivery, and agricultural robotic fleets.
Edge foundation model licenses and per-device runtime royalties targeting our initial 50 enterprise OEM pipeline.
Modular Architecture for Robotics Developers
Robotics engineering teams can integrate our technology as pre-trained foundation weights, custom edge compilation engines, or synthetic training pipelines.
NeuroVoxel-1 (7B / 32B VLA Foundation Model)
Pre-trained weights with open fine-tuning adapters for custom sensor rigs.
EdgeSync TensorRT 10 Compiler
Automated quantization and engine builder producing deterministic sub-8ms binaries.
OmniSynthetic Studio (NVIDIA Isaac Sim Integration)
Procedural USD world generator for rapid domain randomization & zero-shot transfer.
Proven Superiority Across Hardware & Latency
Rigorous testing on NVIDIA Jetson AGX Orin 64GB, NVIDIA H100, and NVIDIA L40S demonstrates clear performance leadership over baseline open models.
High-Margin Software & Royalty Model
Monetizing through per-device edge runtime licensing, annual enterprise SDK access, and cloud training compute subscriptions.
Per-Device Royalty
Embedded runtime license per deployed AMR, drone, or industrial robot camera node. Recurring annual activation.
Annual Platform License
Full access to NeuroVoxel-1 weights, EdgeSync compiler toolchain, and custom hardware calibration assistance.
Synthetic USD Simulation
On-demand GPU-accelerated simulation hours via NVIDIA Omniverse and Isaac Sim cloud orchestration.
World-Class AI Scientists & GPU Architects
Decades of combined experience at the intersection of foundation computer vision, low-latency CUDA computing, and distributed HPC infrastructure.
Dr. Aryan Mehta
PhD Stanford • ex-Perception Lead
12+ CVPR/NeurIPS papers. Deep expertise in transformer models and real-time vision policies for physical robots.
Dr. Elena Rostova
PhD CMU • ex-NVIDIA Fellow
World authority on CUDA micro-benchmarking, FP8 quantization schemes, and TensorRT compilation engines.
Marcus Vance
ex-AWS HPC Architect
Scaled 1024-GPU clusters on InfiniBand networks. Expert in Triton server deployment and low-latency streaming inference.
NVIDIA Inception Partnership & Resource Allocation
Accelerating development of our 70B parameter spatial world model with dedicated NVIDIA compute resources and co-marketing opportunities.
Compute & Hardware Request
- $100K–$250K Cloud Credits: Allocation for NVIDIA DGX Cloud / CoreWeave H100 cluster hours.
- Hardware Dev Kits: 4x NVIDIA Jetson AGX Orin 64GB & Jetson Thor early-access units for edge testing.
- TensorRT Technical Liaison: Direct sync with NVIDIA TensorRT & Isaac Sim engineering teams.
12-Month Key Milestones
- Q2 2026: Pre-train NeuroVoxel-70B on 1 Trillion physical & Isaac Sim USD tokens.
- Q3 2026: Deploy live pilots across 500+ commercial warehouse AMRs with sub-6ms latency.
- Q4 2026: Co-present real-time spatial foundation benchmarks at NVIDIA GTC.