One of the ways I protect the budget for distilling our own LLM is by treating inference cost as a controllable operating expense, not a fixed overhead.
The financial rule is simple:
Do not spend model capacity on decisions that have already become predictable.
Each week, the team targets roughly a dozen heavy Sistem Berasingan. My role is to make sure the research, scoping, and validation effort is matched to a measurable cost-avoidance outcome: lower token burn, fewer billed calls, and cleaner cost attribution.
For complex or ambiguous problems, it is often sensible to use ChatGPT or Claude as a reasoning and research layer first.
The flow then becomes:
Kompleks Problem → Penyelidikan → Penaakulan → Distillation → Deterministic Logic → Smart Routing → DeepSeek V4 Flash / Our Own LLM → Execution
The important financial inflection point sits right after deterministic logik.
If the workload can be resolved with rules, parsers, validation, or structured logik, the Sistem Berasingan executes it directly.
If reasoning is still required, the workload is routed to DeepSeek V4 Flash as the cost-efficient external model.
For specialised, strategic, sensitive, or sovereignty-related workloads, the system routes the task to our own LLM models instead.
This is not a plan to eliminate LLMs.
It is a capital-allocation decision.
Deterministic first.
Cekap Kos LLM when the business case demands it.
Our own LLM when specialised intelligence, control, or data sovereignty matter.
This architecture also changes how we fund model development.
The more workloads we detach, the lower our recurring token and inference spend. That shift turns an open-ended operating cost into a more predictable cost base, which is essential for managing R&D budgets and capital allocation.
The lower that spend becomes, the more budget we can reallocate to model distillation, evaluation, infrastructure, and the broader development of our own LLM ecosystem.
That is the financial operating model behind AINNA NeuralOps, and it is how we keep sovereign, specialised AI affordable untuk PKS Malaysia.
#AINNA #NeuralOps #DetachedSystems #LLM #AIDistillation #SovereignAI #AIInfrastruktur #DeepSeek #ChatGPT #Claude #EnterpriseAI #Automasi
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Dah hantar pada dua orang. Sebab kompleks problem.
The direction on capital-allocation is exactly right.
I read this twice. Our own LLM when specialised is the part that stuck.
Clearer than the vendor decks I get about cost-efficient.
The framing around specialised AI affordable untuk PKS is better than I expected.
You can tell the writer actually worked on cekap kos LLM.
That point about cost-avoidance is well put.
Short and clear. sovereignty-related is worth sending to my team.