The Era of Running LLMs Without GPUs: How to Compete with Major Corporations for 50,000 Yen a Month

Let’s get straight to the point: We are now in an era where environments capable of running LLMs without GPUs can be obtained for 50,000 yen a month.

What does this mean? The assumption that “AI is expensive” is crumbling. When this assumption collapses, the structure of competition changes. There is now a possibility that AI environments run by small and medium-sized enterprises (SMEs) for 50,000 yen a month can stand on the same playing field as the AI infrastructures built by large corporations at the cost of tens of millions of yen.

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Running LLMs Without GPUs: What’s Happening

Until now, if one wanted to run LLMs (large language models) in-house, expensive GPUs like the NVIDIA A100 or H100 were essential. Each unit costs several million yen. If you assembled a server capable of housing multiple GPUs, the initial investment would easily reach 10 to 30 million yen. Renting GPU instances in the cloud would cost tens to hundreds of thousands of yen per month. This was not realistic for SMEs.

However, the situation has recently changed. Optimization technologies for CPU inference, exemplified by llama.cpp, have rapidly evolved, confirming that LLMs with parameter sizes ranging from 7B to 13B can run at practical speeds on Linux servers without GPUs. Advances in quantization (model lightweighting) technology have also been significant. A model quantized to 4 bits can operate sufficiently on a CPU machine with 16 to 32 GB of memory.

Let’s break down the costs:

• VPS (Virtual Private Server): A Linux server with 32 GB of memory and an 8-core CPU can be rented for 30,000 to 50,000 yen per month.
• Models: Open-source models like Meta Llama are free.
• Inference Engines: Free tools like llama.cpp, Ollama, and vLLM are readily available.

In other words, you can have a “dedicated AI environment” for less than 50,000 yen a month. That amounts to 600,000 yen a year. While large corporations invest 30 million yen upfront, SMEs can operate with just 600,000 yen. This 50-fold cost difference signifies the collapse of entry barriers for SMEs.

Of course, the response speed is slower compared to GPU-equipped environments. Where an H100 can produce 100 tokens per second, a CPU might only manage 10 to 20 tokens per second. But consider this: how often do you need “100 tokens per second” for tasks like summarizing meeting minutes, drafting emails, searching manuals, or preparing responses to inquiries? Rarely. Many tasks can be effectively handled at a speed of 10 tokens per second.

If we judge not by “speed” but by “usability,” a GPU-less environment can compete effectively.

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The Story of 2.8 Billion Yen Going Down the Drain: Spending Money Doesn’t Guarantee Victory

Here’s an interesting piece of data. Multiple reports from overseas surveys indicate that despite large corporations investing over 200 million dollars (about 30 billion yen) in AI, there was no noticeable improvement in the quality of on-site operations.

Why is that? The typical pattern is as follows:

1. Executives call for “AI implementation.”
2. They place orders with major system integrators or consultants for tens to hundreds of millions of yen.
3. An “AI infrastructure” is built over six months to a year.
4. The on-site staff don’t know how to use it.
5. Ultimately, no one uses it.

It’s not a matter of money. The failure stems from the lack of clarity on “who, for what purpose, and how to use it.”

The primary reason for the failure of AI investments in large corporations is the “distance from the field.” Decision-makers are too far removed from those who actually perform the tasks. Three months for requirements definition, six months for development, three months for testing. By the time something is released a year later, it is misaligned with the needs of the field. Given the rapid evolution of AI, the design philosophy from a year ago is already outdated.

On the other hand, what about SMEs? When the CEO says, “Let’s try this,” it can be operational by the next day. They listen to the on-site issues and test prompts within the week. If it doesn’t work, they change it the following week. This speed of “trial and adjustment” is the greatest weapon of SMEs.

With an AI environment costing 50,000 yen a month, failures are not painful. If it doesn’t work after three months of testing, they can stop. That’s just 150,000 yen in tuition. While large corporations are bogged down in bureaucratic processes fearing a 30 million yen failure, SMEs can conduct ten experiments.

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AI is Not “Magic That Increases Everyone’s Productivity”

Another crucial piece of data to keep in mind is that several studies from research institutions like MIT and Stanford have pointed out a common finding:

AI does not uniformly increase everyone’s productivity.

Specifically, the following trends are emerging:

• Lower-skilled workers: Productivity significantly improves with AI assistance (reports of 30-40% improvement).
• Higher-skilled workers: Productivity gains are limited, and in some cases, may even decline.
• Types of tasks: The effects are greater for routine and repetitive tasks.

This is actually excellent news for SMEs.

Why? SMEs often face a chronic issue of “lack of specialized personnel.” There are no dedicated marketers. There are no writers. There is only one customer support representative. It is precisely in these “areas lacking skills” that AI can have the greatest impact.

Conversely, if a large corporation introduces AI into a department that already has ten excellent marketers, dramatic changes are unlikely. It’s difficult to further elevate something that is already at a high level.

In other words, the weakness of SMEs—”not enough people”—can be transformed into a strength in the AI era, as they have greater potential for growth.

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So, What Should We Do? A Concrete First Step to Start for 50,000 Yen

Let’s move beyond abstract discussions and outline concrete steps for SMEs to take starting tomorrow.

Step 1: Rent a Server for Less Than 50,000 Yen a Month

Contract a VPS with at least 32 GB of memory and 8 CPU cores. Options include Sakura VPS, ConoHa, or Hetzner (overseas). Use Ubuntu as the OS. This will cost around 30,000 to 50,000 yen per month.

Step 2: Install Ollama and Run Open-Source LLMs

With Ollama, you can install models like Llama 3 or Mistral with a single command. It can operate with minimal technical knowledge. The time required is about 30 minutes. Cost: 0 yen.

Step 3: Apply It to Your “Most Tedious Task”

Choose one task that you do every day and honestly find tedious, such as summarizing meeting minutes, drafting inquiry emails, converting internal manuals into Q&A format, or generating templates for estimates. Don’t seek perfection. There will definitely be tasks where “just providing a draft with 60% accuracy would be helpful.”

Step 4: Use It for Two Weeks and Measure the Results Numerically

Capture data like, “This task used to take 30 minutes a day, but now it takes 10 minutes.” When converted to a monthly basis, that’s a reduction of about 7 hours (20 days x 20 minutes). At an hourly wage of 2,000 yen, that’s 14,000 yen a month. While the 50,000 yen server cost won’t be covered, if you expand the target tasks to three, it could yield 500,000 yen a year. The annual server cost of 600,000 yen would then be almost break-even. If you broaden the scope further, it will definitely turn into a profit.

Step 5: Systematize the “Successful Patterns”

This is the most important part. Avoid making it dependent on individuals. Create a manual stating, “This task is executed with this prompt and this procedure.” Ensure that even if the person in charge leaves, someone else can maintain the same quality starting the next day. The true value of AI is not just in providing intelligent responses but in creating a system where anyone can achieve the same quality.

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The Structure Has Changed: Only Those Who Realize It Will Survive

Finally, I want to convey the most important message of this article.

With the dramatic decrease in AI costs, the equation “capital = competitive power” is beginning to break down.

Three years ago, running LLMs in-house required tens of millions of yen. Now it’s just 50,000 yen a month. This change is not merely a cost reduction; it signifies that the rules of competition itself have changed.

Large corporations are burdened with massive investments as “sunk costs.” Expensive GPU servers, long-term contracts with major vendors, and non-functional internal systems make it difficult for them to pivot quickly.

SMEs, on the other hand, have no such encumbrances. They can start for 50,000 yen, and if it doesn’t work, they can stop. If it does, they can expand. This agility will be the greatest competitive advantage in the AI era.

I want to pose a question: How many tasks in your company could be automated with AI at “60% accuracy”? If there are three, you can start next week. For just 50,000 yen.

You don’t need to do the same things as large corporations. Solve the issues that large corporations overlook at a speed they can’t match and at one-fiftieth of the cost. That is the AI strategy for SMEs.