Zro
Technology

The performance layer behind private AI inference.

MoonMath.ai builds the most efficient, privacy-centric AI inference endpoint.

We are a small team of mathematicians and engineers building fast, private AI inference through low-level algorithms, systems engineering, and hardware-aware optimization.

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Quantization

HyperQuant

HyperQuant is MoonMath's rate-distortion-oriented quantization pipeline for large language and diffusion models. It combines randomized Hadamard transforms, lattice quantization, and entropy coding to compress weights and KV cache while preserving model quality.

  • Weights and KV cache
  • LLM and diffusion models
  • Rate-distortion design
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Kernels

Custom inference kernels

Zro is backed by MoonMath's low-level kernel engineering work: hand-shaped schedules, memory movement, and hardware-aware attention kernels such as the CDNA3 BF16 forward-attention kernel for MI300X.

  • Attention kernels
  • Memory scheduling
  • Hardware-aware optimization
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Hardware

Multi-hardware serving

The product is designed to move across accelerator families rather than depend on one vendor path. We optimize and operate across AMD GPUs, NVIDIA GPUs, and Google TPUs where each model and workload fits best.

  • AMD GPUs
  • NVIDIA GPUs
  • Google TPU
Stack

Compression, kernels, and hardware sit under the API.

Agent layer

Claude Code, Codex, Cursor, Cline, Opencode, Hermes

API layer

OpenAI-compatible requests plus Anthropic-compatible Messages

Privacy layer

EU inference regions with zero request retention by default

Compression layer

HyperQuant-style compression for weights, KV cache, and long-context efficiency

Kernel layer

Custom attention and serving kernels for model-specific throughput

Hardware layer

AMD, NVIDIA, and Google TPU deployment paths

Bring Zro into the tools your developers already use.

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