Jensen Huang's latest NVIDIA announcement reinforces an exciting direction: AI computing is becoming smaller, more powerful and more energy-efficient.
At AINNA, this aligns with our long-term vision for NeuralOps.
✅ WHERE WE ARE TODAY
Our server-based NeuralOps 架构 focuses on doing more with less computing power through:
⚡ 多-阶段 智能路由
⚡ Intelligent Caching
⚡ Specialised Parsers & 分离式 执行
⚡ 本地 SLM/LLM 集成
⚡ 多-代理 编排 (under development)
⚡ WebCLI & Telegram 访问
🚀 WHERE WE ARE HEADING
🟢 第一阶段 - NeuralOps Mini PC
Move NeuralOps from server infrastructure to dedicated PCB-based AI mini computers.
🔵 第二阶段 - Embedded NeuralOps
集成 NeuralOps into increasingly compact ARM/NVIDIA AI computing modules.
🟣 Phase 3 - NeuralOps on Future AI 硅
优化 our intelligence architecture for next-generation miniaturised chips, potentially through semiconductor 合作伙伴hips.
The hardware roadmap is our future direction, not a claim of existing deployment.
想象 中小企业, factories and industrial devices running their own specialised AI 智能体 locally, with minimal cloud dependency.
We're not building our own semiconductor chip today.
We're building an intelligence architecture designed to become smaller as computing technology evolves.
Maximum 智能. Minimum 计算.
今天: 服务器-Based NeuralOps
下一步: PCB Mini PC
Future: Embedded AI → Chip-Level 智能
#AINNA #NeuralOps #NVIDIA #JensenHuang #EdgeAI #AgenticAI #EmbeddedAI #DeepTech #AI基础设施 #马来西亚Tech
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这篇文章适合团队用来开始讨论energy-efficient。
看第二遍才注意到embedded AI的细节。
我会把server-based这一段分享给需要了解技术的同事。
难得有人把next-generation讲得这么直白。
关于🟣 phase 3 - neuralops的说明很完整，基础部分尤其清楚。
还在消化3这一段。