Since deploying OpenClaw at AINNA, our LLM token burn dropped from 34 billion tokens per month to 1.5 billion tokens per month using the Sistem Berasingan approach.
After adding Refactor and Resegment into the pipeline, we pushed that down further to roughly 750 million tokens 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 system 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 tokens, higher cost, slower execution, and unnecessary load across the pipeline.
With Sistem Berasingan, the workload became isolated and focused. With Refactor and Resegment, each process got clear boundaries. The model no longer scans the whole system; it operates only on the scoped segment that actually needs attention.
That is how we moved from:
34B → 1.5B → 750M tokens/bulan
Less context.
Less repetition.
Less waste.
Lower cost.
Lebih Pantas execution.
From a systems-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.
Kecekapan starts with system design.
#OpenClaw #AI #LLM #AIAgents #SystemSeni Bina #TokenOptimization #SoftwareEngineering #AIEngineering #Kecekapan
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الجزء المتعلق بـ 34 جعلني أفكر مطولًا.
The framing around burn dropped from 34 billion is better than I expected.
The case for since deploying openclaw at AINNA is well made.
Still thinking about systems-integration.
We hit our LLM token burn dropped at work before. Good that someone wrote it down.
First piece that handles kecekapan starts with system design honestly.
The figures on further to roughly 750 million make more sense than most posts.
关于34这部分写得很扎实，整体也站得住。