We are a small team of mathematicians and engineers building faster LLM inference through low-level algorithms, systems engineering, custom kernels, and hardware-aware optimization — with a focus on coding models and agentic workloads.
Zro is MoonMath's private inference platform built for coding agents. It gives developers fast access to leading open-weight coding models through a single endpoint, optimized for long-context, multi-turn agentic workloads.
- Built for coding agents: Connect Claude Code, Codex, OpenCode, Cline, and more in minutes.
- Accelerated inference: Our serving stack is engineered for high-throughput, low-latency LLM inference.
- Private by default: Zero request retention and no training on customer data.
- Open models, one endpoint: Run capable open-weight coding models without managing GPUs, inference engines, or serving infrastructure yourself.
- Performance from the stack down: MoonMath combines algorithmic optimization, custom kernels, compression, and hardware-aware deployment to push more performance from modern accelerators.
MoonLite is MoonMath's collection of low-level techniques and tools for making large models faster and more efficient:
- LiteLinear: decomposed modules designed to replace standard FFN layers with more efficient alternatives.
- BackLite: FlashAttention 3-based backward-pass acceleration using sparse gradient approximation.
- LiteAttention: sparse attention techniques for accelerating large generative models.
- LiteRunner: experiment infrastructure for benchmarking and developing model acceleration techniques, with local and Weights & Biases tracking.