Slash compute time - from seconds to nanoseconds.
Accelerate ML, scientific, and numeric workloads with Hologram’s \(O(1)\) geometry-powered compute.
Replace \(O(n^3)\) matrix workloads with constant-time lookups that complete in ~35 ns - no matter your data size. Drop in with PyTorch, JAX, and TensorFlow without rewrites or pipeline friction.
Problem → Solution
Escape cubic overhead with geometry-powered compute.
Stop fighting matrix math bottlenecks. Switch to constant-time execution that keeps pace with your ambition.
Traditional matrix workloads don't scale
Every dimension adds cubic overhead, memory bloat, and floating-point drift. Teams stall while GPU costs climb and iteration slows.
- Cubic complexity that compounds with every new dimension
- Memory-heavy copies and cache misses that waste hardware
- Precision loss that breaks scientific and financial workloads
Geometry-powered, constant-time compute
Hologram replaces iteration with geometry. Constant-time lookups finish in ~35 ns no matter your data size.
- Drop-in support for PyTorch, JAX, and TensorFlow - no rewrites
- Auto dispatch across CPU, CUDA, Metal, and WebGPU
- Zero-copy DLPack interface to keep memory lean
Benefits that turn time into leverage.
Everything you need to deploy constant-time compute, without rebuilding your stack.
True $O(1)$ compute
Scale without slowdown - constant-time operations on any size data.
Multi-backend (CPU, CUDA, Metal, WebGPU)
Auto dispatch to the right hardware for every workload.
Zero-copy memory
Lean and efficient - keep data in place with DLPack interoperability.
Exact arithmetic
Full precision for science and finance without FP error creep.
Parallel by design
Built for modern hardware and data-parallel execution.
Plug-and-play
Integrate in minutes with PyTorch, JAX, and TensorFlow.
Proof it works.
Open-source, community-driven, and trusted by leading researchers and HPC labs worldwide.
“Hologram cut our training time by 80%. What used to take hours now finishes in minutes.”
ML Infra Lead
Quantum AI Research
Enterprise research lab
How Hologram works.
From data to constant-time execution in four steps - no rewrites required.
Data canonicalization
Structure your tensors with a geometry-based transform that preserves precision.
$O(1)$ lookups
Replace heavy iteration with constant-time access patterns (~35 ns).
Zero-copy interface
Connect via PyTorch, JAX, and TensorFlow with zero-copy DLPack exchange.
Auto backend dispatch
CPU, GPU, or WebGPU with data-parallel execution - all automatic.
Meet the Community
Researchers, developers, and visionaries building the future of universal data infrastructure.
Community Projects
Innovative projects built using UOR principles and technologies.
Atlas: Universal Mathematical Language
by UOR Foundation
A breakthrough showing how five exceptional Lie groups derive from a single 96-vertex construct. The Golden Seed Vector reveals the universal mathematical language for generating complexity with mathematical certainty.
Atomic Language Model
by UOR Foundation
A mathematically rigorous language model implementing Chomsky's Minimalist Grammar. Uses Coq-verified proofs and probabilistic rule weighting for infinite syntax generation.
Ready to build at the speed of math?
Ship \(O(1)\) compute today. Open-source, community-driven, and free forever.