Conclusion

Let’s get straight to the point. A technology has emerged that reduces the “operational cost” of AI to one-hundredth.

There are large corporations spending 5 million yen a month to run GPU clusters and operate large language models (LLMs). Meanwhile, a system is emerging that can achieve nearly the same results for just 50,000 yen a month.

A cost difference of 100 times. What happens when this gap is closed?

The strategy of large corporations to “overwhelm with financial power” becomes ineffective. Small and medium enterprises (SMEs) can stand on the same playing field. In fact, they may even have an advantage due to their agility.

The key to this transformation lies in the combination of the LOOP Skill Engine and the GPU-Free Local LLM Execution Environment.

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What is the LOOP Skill Engine?—The Concept of “Not Making AI Think Every Time”

First, let’s summarize the essence of the LOOP Skill Engine in one sentence.

“Record the successful operation of AI once, and then replay that recording from then on.”

That’s all there is to it. However, this concept dramatically changes the cost structure.

Traditional LLM-based AI agents query the LLM every time they perform a task. They consume tokens and incur API costs with each request. Moreover, since LLMs operate probabilistically, the results can vary even with the same instructions. There’s no guarantee that you will get the same result if you run the task ten times.

The LOOP Skill Engine fundamentally changes this.

1. First Execution: The AI agent executes the task and records all pathways of tool invocation.
2. Skill Generation: It automatically generates parameterized “loop skills” from the recording.
3. Subsequent Executions: It bypasses the LLM and deterministically replays the recorded skills.

In other words, after the first execution, the LLM is not called. Tokens are not consumed. API costs do not apply.

According to published figures, there is a 99% reduction in token consumption and a 99% success rate for tasks. This figure is particularly relevant for agent tasks that are repeated in cycles of 5 minutes to 24 hours—such as regular data collection, report generation, inventory checks, and first-level inquiry routing.

Why is This a “Game-Changer” for SMEs?

The biggest bottleneck for SMEs in utilizing AI is the running costs.

While initial setup costs can sometimes be managed with subsidies, the monthly API usage fees, cloud GPU charges, and personnel costs for operation and maintenance accumulate, leading to many cases where businesses conclude, “We just can’t afford it.”

The structure of the LOOP Skill Engine directly addresses this issue. It uses the LLM only for the first execution, and thereafter relies on “recorded playback.” Even if executed tens of thousands of times a month, the token cost is nearly just the initial execution.

A world where the API costs that used to be 500,000 yen a month are reduced to just 5,000 yen.

This is not just a story of “a little cheaper.” The cost structure itself is changing.

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Running LLMs Without GPUs—The Era of “You Need Expensive GPUs to Use AI” is Over

Another significant change is the emergence of GPU-free LLM execution environments.

Traditionally, running LLMs locally required high-performance GPUs. GPUs like the NVIDIA A100 or H100 cost several million yen each. Setting up a cluster could cost tens of millions. While large corporations can procure these without hesitation, it is not realistic for SMEs.

However, technology for executing LLMs on CPUs is rapidly maturing. Advances in quantization (a technique that reduces model precision to lighten the load) mean that models in the 7B to 13B parameter class can run sufficiently on a standard Linux server with 16GB of memory.

Let’s look at the specific cost implications.

Take a look at this table. There is a difference of over 100 times in initial investment and 100 times in monthly costs. Yet, the LOOP-based model outperforms in task success rates.

Those who spend money are losing. Such a reversal is structurally beginning to take place.

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The Significance of Ollama v0.25—Lowering the “Final Hurdle” for Local LLMs

Ollama, which is gaining attention as a local LLM execution environment, has implemented speed improvements in its release build with v0.25.0-rc0.

While this may seem like an update for developers, its essential significance is different.

“The introduction and updating of local LLMs have become faster and easier.”

The initial execution of the LOOP Skill Engine requires an LLM. If this LLM relies on a cloud API (like OpenAI), costs will ultimately be incurred. However, by using Ollama to set up a local LLM, the cost of the initial execution can approach zero.

In other words, a combination of LOOP × Ollama × standard Linux server creates a structure where the operational costs of AI agents approach nearly zero.

Only electricity costs and server depreciation remain. Monthly costs of 30,000 to 50,000 yen.

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So, What Should We Do?

I don’t want this to end with “That’s an interesting technology.” Here are three things SMEs should consider starting today.

1. Inventory Your Company’s “Repetitive Tasks”

The LOOP Skill Engine shines in tasks that are performed periodically. Daily data aggregation, weekly report generation, hourly inventory checks—listing up these “jobs that humans are doing but don’t need to” is the first step.

2. Experiment with a “GPU-Free” Environment on a Small Scale

Ollama is free to use. You can acquire a used Linux server for just a few thousand yen. Start by setting up one server and running a 7B-class model. Experiencing “AI can run in such an inexpensive environment” will enhance your decision-making accuracy.

3. Don’t Wait for “AI Democratization”—Go Get It Yourself

Before large corporations notice this technology and start optimizing it, SMEs should act first. Agility is the greatest weapon of small and medium enterprises. Large corporations take three months for approvals. SMEs can start testing next week.

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What Will Happen Next?

To be honest, the figures of “99% success rate and 99% token reduction” for the LOOP Skill Engine may vary depending on the types and conditions of the tasks involved. It is not universal. For tasks requiring complex decision branches or those that need to flexibly respond to different inputs each time, traditional direct execution of LLMs may still be more suitable.

However, the structural direction is clear.

“From making AI think every time to making it think once and then replaying it.”
“From needing to buy expensive GPUs to being able to run sufficiently on inexpensive CPUs.”

When these two trends converge, the operational costs of AI will dramatically decrease. And when costs drop, the greatest beneficiaries will not be large corporations. It will be the small and medium enterprises that have been unable to engage due to cost barriers.

A 5 million yen AI and a 50,000 yen AI. The performance gap is narrowing, while the cost difference remains 100 times.

Which will survive is already clear.

For SMEs, the era of “AI being too expensive to engage with” is coming to an end. The question is whether they will realize this and take action.