Physical AI Foundation Tier • Embodied Robotics
CUDA 12.6 • TensorRT 10 • Isaac Sim

Physical AI & Real-Time
4D World Foundation Models

VoxelDyn Labs synthesizes sub-4ms physical spatial intelligence for autonomous robots and spatial digital twins. Transforming multi-modal camera arrays into predictive 4D Gaussian kinematics powered by high-throughput Tensor Cores.

Inference Engine TensorRT-LLM FP8
Actuation Latency 3.74 ms
Real-Time Throughput 144.2 FPS
Edge Target Jetson AGX Orin 64GB
Live Spatial Inference Playground

Sub-4ms 4D Gaussian Kinematic Perception

Test VoxelDyn's multi-modal sensory pipeline in real-time. Switch between native photorealistic RGB camera feeds, 4D Gaussian Splatting point meshes, metric depth estimation, and physical collision voxels.

MODE: 4D Gaussian Splatting ACCELERATOR: DGX H100 Cluster TENSOR LOAD: 94.8%
// VOXELDYN REALTIME PHYSICAL TELEMETRY
STATUS: SYNCHRONIZED [LOCK]
GAUSSIAN_PRIMITIVES: 1,420,800
STREAM_STATUS: ACTIVE
TensorRT 10.0 • FP8 Quantized • Isaac Sim Compatible
Deep Tech Foundation

Engineered for High-Throughput Spatial Acceleration

VoxelDyn is built from the ground up to exploit modern accelerated computing architectures, achieving zero-copy memory transfers and microsecond physical dynamics.

VoxelDyn Core™ WFM

A 14B parameter spatial foundation model trained on 4D Gaussian splats. Predicts real-world object dynamics, physical collisions, and friction coefficients at 144 FPS.

TensorRT-LLM FP8 Transformers H100 SXM5

Isaac Sim & Omniverse SDK

Bi-directional OpenUSD integration with Isaac Sim. Generates millions of synthetic domain-randomized training scenarios to bridge the sim-to-real robotic gap.

Omniverse USD Isaac Lab Synthetic Data

EdgeRT Runtime for Jetson

Ultra-low power embedded deployment engine. Compiles spatial world representations down to 15W envelopes on Jetson AGX Orin for humanoid robots and drones.

Jetson Orin 64GB DLA Accelerator Sub-15W
Infrastructure Sizing

GPU Cluster & Compute Estimator

Mathematical modeling for deep-tech scaling. Calculate model scale, simulated agent concurrency, and required GPU infrastructure.

World Foundation Model Parameters 14B Parameters
Concurrent Autonomous Robot Fleet 1,000 Active Robots
Synthetic Simulation Speed 120 FPS Simulation

*Compute methodology calibrated to 3D parallelism and multi-GPU distributed physics benchmarks.

Estimated GPU Compute Budget Validated
$200,000
Cloud GPU Compute Infrastructure
Cluster Configuration: 16x H100 SXM5 Nodes (128 GPUs)
Total Training Hours: 41,600 GPU Hours
Total Cluster VRAM: 10.2 TB Aggregate VRAM
Interconnect Bandwidth: 115.2 TB/s NVLink 4.0
Sole Executive Leadership

Meet the Founder & CEO

VoxelDyn Labs is led by a solo technical founder with deep expertise in real-time CUDA acceleration, 4D neural radiance fields, and robotics simulation.

Sole Founder & CEO

Sam Harrison

Founder & Chief Executive Officer

Sam Harrison is a computer vision and robotics researcher formerly at Stanford AI Lab with over 8 years of specialized experience authoring high-throughput CUDA kernels, 4D Gaussian dynamics, and real-time physical simulation engines. Prior to founding VoxelDyn Labs, Sam led GPU acceleration initiatives for autonomous perception systems, contributing to major breakthroughs in sub-5ms sensor-to-actuation pipelines.

Stanford AI Lab • MS Robotics
6+ CVPR / NeurIPS Papers
2 Core Physical AI Patents
Investor & Technical Materials

Explore the 10-Slide Interactive Pitch Deck

Detailed breakdown of market TAM ($68.4B), proprietary 4D Gaussian kinematics moat, simulation pilot benchmarks, and our compute scaling strategy. Includes 1-Click PDF export.

Launch Interactive Presentation Read Technical Whitepaper
Closed Alpha Access

Request Early SDK & Isaac Sim Access

Deploy VoxelDyn 4D World Models directly to your Jetson or DGX cluster.

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