MoonMath.ai builds the most efficient, privacy-centric AI inference endpoint. 构建物理AI的性能引擎
We are a team of mathematicians and engineers building fast, end-to-end private AI inference through low-level algorithms, systems engineering, and hardware-aware optimization. 我们是一支精干的数学家与工程师团队，通过底层算法、系统工程与硬件优化，为下一代AI系统打造生产级加速方案。
“MoonMath delivered meaningful performance gains for our image inference workloads. Their low-level optimizations translated directly into faster generation and better GPU efficiency.”
“MoonMath为我们的图像推理带来了显著的性能提升。他们的底层优化带给我们更快的生成速度和更高的GPU效率。”
“We partnered with MoonMath to optimize our BADAS world model. Within a few weeks, they introduced a novel algorithm with promising potential for significant latency reduction.”
“我们与MoonMath合作，优化我们的 BADAS 世界模型。在短短几周内，他们提出了一种新颖的算法，展现出显著降低延迟、提升性能的强大能力。”
“For LTX-2, MoonMath demonstrated a communication solution that significantly outperformed NCCL, showing the kind of low-level GPU expertise that can materially improve inference performance.”
“对于 LTX-2,MoonMath 展示了一种显著优于 NCCL 的通信方案,体现出能够实质性提升推理性能的GPU底层优化能力。”
“MoonMath impressed us with their unique ability to identify the high-level bottlenecks of diffusion transformer systems, develop intuition for promising areas of optimization, and bring the technical depth in low-level AI performance needed to materialize these improvements across major GPU vendors. Their work sits exactly at the intersection of kernels, systems, and model efficiency that matters for scaling diffusion and world-model inference.”
“MoonMath 让我们印象深刻。他们身上有一种难得的综合能力:既能精准识别扩散 Transformer 系统的高层瓶颈,对哪些优化方向最有潜力有着敏锐的直觉,又有底层 AI 性能优化的深厚功底,能把这些改进真正落地,并覆盖主流的各家 GPU 厂商。他们的工作恰好处在 kernel、系统与模型效率的交汇点上——而这正是把扩散模型与世界模型推理推向更大规模的关键所在。”