NeuroTensor Labs
Real-Time Spatial World Models for 100 Million Autonomous Robots
Developing sub-8ms Vision-Language-Action (VLA) foundation models accelerated natively on low-latency TensorRT runtimes, CUDA-X, and physics simulators 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 Edge AGX and next-gen 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 for High-Throughput Accelerated Computing
Every layer of the NeuroTensor pipeline leverages hardware-aware software libraries and GPU 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 (Physics Digital Twin & USD Sim)
Procedural USD world generator for rapid domain randomization & zero-shot transfer.
Proven Superiority Across Hardware & Latency
Rigorous testing on Edge AGX Orin 64GB, Enterprise 80GB SXM5, and Enterprise 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 cloud physics simulation and digital twin orchestration.
Executive Leadership & Direction
NeuroTensor Labs is founded and led by Tanaji Vishnu Yadav, directing core spatial AI architecture, low-latency GPU acceleration, and enterprise commercial deployment.
Tanaji Vishnu Yadav
Lead AI Systems Architect • ex-Stanford AI Lab
Pioneer in low-latency spatial geometry models and embodied robotics vision. 12+ peer-reviewed papers across CVPR, ICCV, and NeurIPS with 2,400+ citations.
Foundation Model Architecture & VLA
Directs the design of NeuroVoxel spatial transformer weights, multimodal tokenization, and end-to-end 6-DoF trajectory planning for autonomous mobile robots.
GPU Acceleration & TensorRT Compilation
Oversees FP8 SmoothQuant compiler pipelines and CUDA 12.6 kernel execution on Edge AGX Orin, Thor, and 80GB SXM5 clusters.
Commercial Partnerships & Inception Roadmap
Leading the Frontier DeepTech Accelerator cohort application, hardware pilot deployments across commercial robotics OEMs, and company capitalization.
Capitalization & Compute Expansion Roadmap
Accelerating development of our 70B parameter spatial world model with dedicated ultra-scale compute resources and co-marketing opportunities.
Compute & Hardware Request
- $100K–$250K Cloud Credits: Allocation for Distributed 80GB SXM5 GPU cluster hours.
- Hardware Dev Kits: 4x Edge AGX Orin 64GB & Next-Gen early-access dev units for edge testing.
- TensorRT Technical Liaison: Direct sync with low-latency TensorRT & physics simulation compiler 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: Present real-time spatial foundation benchmarks at premier AI & robotics summits.