Since deploying OpenClaw at AINNA, our LLM token burn dropped from 34 billion 令牌 per month to 1.5 billion 令牌 per month using the 独立系统 approach.
After adding Refactor and Resegment into the pipeline, we pushed that down further to roughly 750 million 令牌 per month.
The key takeaway from an engineering standpoint: optimization is not always about scaling up GPUs or throwing more compute at the problem. The real gains come from redesigning how the 系统 ingests context, routes tasks, and executes work.
Before this architecture change, the AI had to re-read large amounts of context every time it needed to make a small change. That repeated scanning created wasted 令牌, higher cost, slower execution, and unnecessary load across the pipeline.
With 独立系统, the workload became isolated and focused. With Refactor and Resegment, each process got clear boundaries. The model no longer scans the whole 系统; it operates only on the scoped segment that actually needs attention.
That is how we moved from:
34B → 1.5B → 750M 令牌/月
Less context.
Less repetition.
Less waste.
更低 cost.
更快的执行.
从 a 系统-integration perspective, this confirms what we see in the field: the future of AI efficiency is not just stronger hardware. It is about smarter architecture, cleaner context management, and modular execution pipelines.
效率 starts with 系统 设计.
#OpenClaw #AI #LLM #AIAgents #系统架构 #Token优化 #Software工程 #AI工程 #效率
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文章对从 a 系统-integration perspective的结论比较平衡，不只是强调好处。
难得有人把since deploying openclaw at AINNA讲得这么直白。
关于burn dropped from 34 billion的说明很完整，基础部分尤其清楚。
关于our LLM token burn dropped的例子很实用，适合团队继续讨论。
看第二遍才注意到750 million 令牌 750 million的细节。
our LLM token burn dropped这个说法我要拿回去跟同事讨论。
这篇内容让我更容易理解为什么从 a 系统-integration perspective值得关注。
这篇文章把burn dropped from 34 billion讲得比一般的AI介绍更具体。