Conclusion First: “Using it Because It’s Free” May No Longer Be Viable

Nvidia’s acquisition of Hugging Face for $12.9 billion (approximately 1.9 trillion yen)—if this report is true, it represents the most impactful AI news for local small businesses.

Why? Hugging Face is a “library of models” where AI models from around the world are gathered. Over a million models are available, many of which are free to use. Even a small company with five employees in a rural area can download the same models as a large corporation in Tokyo and incorporate them into their operations. This “structure that allows access to cutting-edge technology for free” has been the greatest weapon for small businesses.

Now, that library has been acquired by Nvidia, a dominant player in the GPU market.

The question is simple: Will “free” continue?

Considering Nvidia’s Reasons for the Acquisition Reveals the Answer

Nvidia is a semiconductor company. Their AI GPU, the “H100,” costs over 4 million yen each. Revenue from data centers has exceeded 3.6 trillion yen in a single quarter. Their business model is to “sell the hardware that powers AI.”

So why acquire Hugging Face, a software platform?

The answer is to “vertically integrate the AI supply chain.” Distribution of models (Hugging Face) → Model training and inference (NVIDIA GPU) → Cloud execution environment (DGX Cloud). Nvidia will control everything from entry to exit.

This structure is similar to how Google made its search engine free, created a browser (Chrome), distributed an OS (Android), and recouped costs through advertising. For Nvidia, Hugging Face is the “entry point,” with the main focus on promoting GPU and cloud sales.

In other words, whether or not the models themselves become paid is not the essence. The real issue is whether it will become difficult to operate without Nvidia GPUs and whether users will be directed to Nvidia’s cloud, representing a more structural form of lock-in.

How Will Costs Change for Small Businesses?—Estimating with Three Scenarios

Currently, a typical cost structure for a small business with about 10 employees using Hugging Face’s open-source models to implement AI is as follows:

• Model download: 0 yen
• Fine-tuning (adjusting with company data): Cloud GPU usage costs 10,000 to 30,000 yen per month
• Inference (actual operation): A few thousand yen to 20,000 yen per month
• Initial development and setup costs: Outsourcing costs 500,000 to 1.5 million yen, or just labor costs if done in-house

Annual running costs roughly amount to 150,000 to 500,000 yen. This is why small businesses have been able to use AI.

So, what will happen after the acquisition?

Scenario A: Status Quo (Probability 20%)

Distribution of open-source models continues as before, with no cost changes.

However, this seems unlikely. No company would spend 1.9 trillion yen to “change nothing.” Nvidia’s shareholders would not allow it.

Annual Cost: 150,000 to 500,000 yen (no change)

Scenario B: Freemium Model (Probability 50%)

The most likely scenario. Basic model downloads remain free, but the following will become paid:

• Access to high-performance models: The latest large models like Llama and Mistral will be available “only via Nvidia Cloud.”
• Inference API: The current Hugging Face Inference API will see a price increase, becoming a subscription of 30,000 to 100,000 yen per month.
• Fine-tuning environment: Optimization will only be possible on Nvidia DGX Cloud.

In this case, small business costs would look like this:

• Model usage: 0 yen (basic model) to 50,000 yen (high-performance model)
• Inference API: 30,000 to 100,000 yen per month
• Fine-tuning: 50,000 to 150,000 yen (using DGX Cloud)

Annual Cost: 1,000,000 to 3,600,000 yen. 3 to 7 times the current cost.

Even at 50,000 yen per month, that’s 600,000 yen annually. Using multiple models could easily push costs over 1 million yen. For a company with 10 employees, an annual AI running cost of 3 million yen would lead many to conclude that it is “not feasible.”

Scenario C: Complete Lock-In (Probability 30%)

If Nvidia seriously advances vertical integration, major models on Hugging Face will become “NVIDIA-certified models” guaranteed to work only on Nvidia GPUs. Operation on AMD, Intel, or Apple Silicon will be deemed “not recommended.”

In this case, the issue shifts from cost to a lack of options.

• Owning Nvidia GPU servers: Initial costs from 5 million yen
• Using DGX Cloud: Monthly fees from 300,000 yen
• Switching to APIs like OpenAI: Monthly fees from 50,000 to 200,000 yen (but without the ability to train on company data)

Annual Cost: 600,000 to over 3,600,000 yen. Plus the risk of “not being able to control it”.

What Will Small Businesses Do When “Free” Ends?

Now we get to the main point.

For large corporations, an annual AI cost of 3 million yen is negligible. However, for small businesses, it equates to the salary of one employee. It becomes a choice between “using AI or hiring someone.”

But there’s no need to be pessimistic. If action is taken now, the increase in costs can be minimized.

Three Things to Do This Month

1. Save the Models You Are Using “Right Now” Locally

The most important and least costly measure. By saving the models downloaded from Hugging Face locally, you won’t be affected by policy changes after the acquisition.

Specifically:

• Backup the weight files of the currently used models to your company server or NAS.
• Record the model versions and license conditions in a text file.
• Time required: 1 to 2 hours. Cost: 0 yen

Whether or not you do this will drastically change your options six months from now.

2. Secure Alternative Procurement Routes Besides Hugging Face

Hugging Face is the largest hub, but it is not the only one.

• Ollama: A tool for running LLMs locally. Easy to download and execute models.
• GitHub / GitLab: Many researchers publish model weights directly.
• Kaggle Models: Under Google, but growing as a model distribution platform.
• ONNX Model Zoo / TensorFlow Hub: Framework-specific, but can serve as alternatives.

The risk of relying on a single platform has been made clear by this situation. Always have at least two procurement sources. This is the same principle as not relying on a single supplier in business management.

3. Identify Tasks That Can Be Handled by “Small Models”

This is where small businesses can gain an advantage.

Large corporations seek “top-performing models”—massive models with 70 billion or 100 billion parameters. These require Nvidia’s expensive GPUs.

However, small business tasks—summarizing meeting minutes, drafting emails, automating FAQ responses, extracting data from invoices—can be effectively handled by small models with 7 billion to 13 billion parameters.

Advantages of small models:

• Can run on a local PC (16GB memory). No cloud needed.
• No need for Nvidia GPUs. They run smoothly even on Apple Silicon Macs.
• Running costs: only electricity.

In other words, no matter how Nvidia tries to lock in users, as long as small models are run locally, they will not be affected. “Running small” is the strongest defensive strategy for small businesses.

What’s Truly Scary Is Not the “Acquisition” but the “Dependency”

The essence to consider from this report is not that Nvidia is bad or that Hugging Face is changing.

The problem lies in our structure of depending on a single platform simply because it was “free.”

This applies to the cloud as well. What if AWS raises its prices? What if Google shuts down a service? These scenarios can happen at any time.

For small businesses to effectively utilize technology, it’s not about chasing the latest tools. It’s about designing for “what to do when it becomes unavailable.”

Holding models locally. Securing multiple procurement sources. Establishing a system that can operate with small models.

All three of these can be done this month at no cost.

It will be too late once the acquisition is officially confirmed. Before “free” ends, create a system that does not rely on “free.” That is what needs to be done now.