分离式系统 Lighten the LLM 负载✎ Edit

To keep the economics of distilling our own LLM sustainable, I spend most of my engineering time building and deploying 分离式系统.

The production rule is simple:

Do not route predictable work through an LLM.

I aim to ship about a dozen heavy 分离式系统 each week. Each one requires workflow analysis, pattern extraction, rule definition, and field validation before it goes 实时.

For the harder problems, I usually start with ChatGPT or Claude as a higher-level reasoning and research layer to shape the solution.

The pipeline then looks like this:

复杂 问题 → 研究 → 推理 → 蒸馏 → 确定性 Logic → 智能路由 → DeepSeek V4 Flash / Our Own LLM → 执行

The critical engineering decision is what happens after the deterministic logic stage.

If rules, parsers, validators, or structured logic can resolve the workload, the 独立系统 executes it directly.

If more reasoning is required, the workload is routed to DeepSeek V4 Flash as a cost-efficient external model.

For specialised, strategic, sensitive, or sovereignty-related workloads, we route the task to our own LLM models instead.

This is not about eliminating LLMs.

It is about routing work to the right processor at the right time.

确定性 logic first.
成本-efficient LLM when reasoning is required.
Our own LLM when specialised intelligence, control, or sovereignty matters.

This routing architecture is what makes the economics of owning our own models work.

The more workloads we detach from the LLM path, the lower our recurring token and inference spend.

The lower that spend becomes, the more budget we can redirect into model distillation, evaluation, infrastructure, and the broader development of our own LLM ecosystem.

That is the operating model behind AINNA NeuralOps.

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