ZibraXYZ
ZibraXYZ: the data layer for physics AI
Compressing, streaming, and serving volumetric data
directly to your GPUs.
Eliminate I/O bottlenecks.
Increase GPU utilization.
Train physics models faster.
Problem
Data gravity is holding back your training pipeline
Physics simulations generate massive, complex datasets — and every stage of your pipeline pays the tax.
Storage
Retaining full-resolution datasets across runs is unsustainable. Teams downsample or discard data.
I/O Bottleneck
Data transfer between storage and GPU is the limiter. GPUs sit idle waiting for data.
Format Fragmentation
VTK, HDF5, OpenVDB, custom formats — each needs its own pipeline and maintenance.
Collaboration
Sharing multi-TB datasets across teams means days of transfers and duplicated infra.
Solution Overview
One Data Layer. Raw Simulation to GPU-Ready Tensors.
Data Loader
Simulation
HDF5
VTK
VDB
ZARR
Custom Formats
Sensors
LAS
E57
PTS
Custom Formats
DATA REPRESENTATION
Structured
Volumetric Grids
General Tensors
Unstructured
Point Clouds
Surface Meshes
Gaussian Splats
Volumetric Meshes
LOSSY COMPRESSION
Adaptive Compression
Strategies
GPU-native algorithms
600 GB/s decompression
Strategy auto selection
Per-field compression
Topology compression
Compression Parameters
Optimisation
Optimal compression parameters for statistical characteristics of datasets
LOSSLESS COMPRESSION
Strategy auto selection
OUTPUT
PyTorch
Tokenizer
Use Cases
Built for the Data You Actually Work With
Designed for teams running large-scale physics simulations across domains.
CFD & Fluid Dynamics
Turbulence, multi-phase, LES/DNS
Climate & Weather
Atmospheric, ocean, reanalysis
Structural Mechanics
Stress/strain, crash, fatigue
Electromagnetics
Field distributions, plasma
Multi-Physics
Coupled heterogeneous fields
Whether you're training surrogate models or building foundation models across domains — ZibraXYZ handles the data layer so you can focus on the model.
Technical Specs
Under the Hood
GPU-Native Decompression
Universal Format Support
Adaptive
Encoding
Benefits
What Changes for Your Team
01
Faster Training
Remove I/O as the bottleneck. Keep GPUs saturated and reduce time-to-convergence.
02
Lower Costs
10–30× compression reduces storage and bandwidth by an order of magnitude.
03
Standardized Pipelines
One format across datasets, teams, and projects. No format-specific preprocessing.
04
Foundation Model Ready
Scale to multi-domain datasets and trillion-token training regimes.
On the Roadmap
ML-Ready Tokenization
We're building a native transformation layer — turning compressed simulation data directly into model-ready tokens.
Zibra AI is backed by
Built by world-class specialists in volumetric data compression. Our technology is proven in production VFX pipelines — now purpose-built for physics AI, where data challenges are larger and performance requirements are just as unforgiving.
Your Data Pipeline
Shouldn't Be Your Bottleneck
Tell us what you're working on. Our engineering team will show you how ZibraXYZ fits into your stack.
Book a 30-minute technical call.
No sales pitch — just engineering.
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