Conclusion

Let’s get straight to the point: the era of buying expensive GPUs is coming to an end.

To run AI in-house, high-priced GPUs have been a necessity. This common belief is quietly crumbling.

AI models compressed using a technique called 4-bit quantization are demonstrating performance equivalent to, or in some cases exceeding, that of full precision (16-bit) models. Benchmark results published by Hugging Face illustrate this.

The implication is straightforward. The required GPU memory is reduced to one-fourth. In other words, the money spent on GPUs will also approach one-fourth.

Imagine a world where GPU costs that used to be 100,000 yen per month drop to 20,000 to 30,000 yen per month. For small and medium-sized enterprises (SMEs), this fundamentally changes the criteria for deciding whether to use AI.

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What is 4-Bit Quantization?

Let’s skip the complicated details and get to the essence.

The core of an AI model consists of a vast collection of numerical values (parameters). Typically, these values are stored with a precision of 16 bits (FP16) or 32 bits (FP32). Using 16 bits per value consumes that much memory. For a model with 7 billion parameters, approximately 14GB of GPU memory is required in FP16.

4-bit quantization is a technique that reduces the precision of these values from 16 bits to 4 bits. In simple terms, this cuts memory usage to one-fourth. A model with 7 billion parameters can run on about 3.5GB.

“If you lower the precision, the performance will drop too,” is the common assumption. However, with the latest quantization methods (such as GPTQ, AWQ, QuIP), clever techniques are employed during compression to retain important information, leading to benchmark scores that are on par with or even exceed those of FP16.

Why does it sometimes “surpass”? One hypothesis is that quantization acts as a form of regularization (suppressing overfitting), thereby enhancing generalization performance. While theoretical explanations are still in progress, the fact that multiple reports indicate “4-bit has surpassed full precision” is significant.

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Looking at the Numbers: How Much Will GPU Costs Drop?

Now, let’s discuss what matters most to SMEs: the specific numbers.

In the Case of Cloud GPUs

These are rough estimates for major clouds like AWS, GCP, and Azure, but processing that used to cost 100,000 yen per month in FP16 can drop to 20,000 to 30,000 yen with 4-bit quantization. This represents a cost reduction of 70-80%.

Moreover, if only 3.5GB is needed, there may be cases where cloud GPUs are unnecessary altogether. A local PC GPU (like the GeForce RTX 3060 with 12GB, which retails for around 40,000 to 50,000 yen) can run it just fine. This creates a scenario where monthly costs are zero, with an initial investment of 50,000 yen.

In-House Server Case

The price drops from 3,000,000 yen to 50,000 yen. This price difference is what separates “considering” from “acting immediately” for SMEs.

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The Apple Neural Engine: The Reality of Edge AI

There is another interesting piece of empirical data: the performance of quantized models on the Apple Neural Engine (ANE).

FP16 models cannot run on the ANE and fall back to CPU processing. However, when quantized to int8 or 4-bit/2-bit, they can run on the ANE, achieving an ANE residency (the proportion of processing done on the ANE) of about 83%. As a result, the decoding speed improved by 1.8 to 2.2 times.

What does this mean?

Practical-speed AI inference can run on Mac mini or iPad.

The Mac mini M2 costs about 100,000 yen. If this can be used as an “AI inference machine,” the cost of AI adoption for local SMEs will fundamentally change. There’s no need to build a dedicated GPU server. A Mac mini sitting next to the CEO’s desk can handle summarizing internal documents, automatically classifying inquiries, and generating drafts for estimates.

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“So, where should we start?”

Listing abstract introduction steps is meaningless. Let’s write it at a level where SMEs can “get started tomorrow.”

Step 1: Experience it with “llama.cpp” (Time Required: 30 minutes, Cost: 0 yen)

llama.cpp is an open-source tool that allows you to run quantized models using only a CPU. Download the 4-bit quantized GGUF model from Hugging Face and run it on your PC. It supports Windows, Mac, and Linux, and can run without a GPU.

The first step is to experience it. Gaining the sense of “Wow, it runs this smoothly” is the starting point for everything.

Step 2: Apply it to Your “Most Tedious Task”

You don’t need to think about full company implementation right away. First, choose one repetitive task that consumes the most time.

• Classifying inquiry emails
• Summarizing meeting minutes
• Drafting estimates
• Searching and answering manuals

Apply a locally running quantized model to that task. If the accuracy is insufficient, switch models. If it’s sufficient, integrate it directly into your operations.

Step 3: Calculate Costs and Decide Whether to Buy or Rent

How many hours will you use it per month? How many cases will you process? Based on this, you can determine whether to stick with a local PC (initial investment of 50,000 yen, monthly cost of 0 yen) or opt for a cloud GPU (monthly cost of 20,000 – 30,000 yen).

What’s important is that the discussion has shifted from “Should we adopt AI?” to “Which configuration is the cheapest?” 4-bit quantization has established this premise.

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The True Meaning for SMEs

Large companies can line up dozens of A100s to run massive models. That’s a battle of capital strength, and SMEs cannot compete on the same level.

However, 4-bit quantization changes the structure.

If a 7 billion parameter model can run on a 50,000 yen GPU, the gap in “AI inference costs” between large companies and SMEs becomes negligible. The differentiator will not be the size of the model, but rather the ideas and execution speed regarding “how to apply it to one’s own business.”

Here, SMEs have the advantage. The distance between decision-makers and the field is shorter. “Let’s try it next week” is feasible. Compared to large companies that take three months for approvals, SMEs are at a significant advantage.

When technology becomes cheaper, it is not the “companies with money” that win, but the “companies that move quickly.”

4-bit quantization has finally opened up the prerequisites for “moving quickly” to SMEs as well.

All you need to try is 30 minutes and your existing PC.

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Caution: Quantization is Not Universal

Finally, let’s discuss a sober point.

Is 4-bit quantization universal? Not necessarily. There are cases where accuracy may drop depending on the task. In particular, for tasks requiring numerical calculations or complex reasoning, differences from FP16 can be more pronounced. Additionally, fine-tuning (additional training) is challenging to perform directly on quantized models, necessitating methods like QLoRA.

That’s why it’s crucial to “try first.” Whether it can be used for your tasks can only be judged through real business operations, not benchmarks. Fortunately, the cost of trying is nearly zero.

Try it, and if it works, adopt it. If it doesn’t, try another model. This cycle can be run at a monthly cost of 0 yen. That, in itself, is the greatest value brought by 4-bit quantization.