In recent years, with advancements in technology, generative AI services — ranging from text and images to videos — have spread globally. It is expected that their commercial applications will continue to grow. Among these, large language models, or LLMs, have become indispensable in the realm of text-based generative AI.

What are LLMs?

LLMs are a type of natural-language-processing (NLP) model. They are built using large datasets and the machine learning method known as deep learning. This ‘language model’ focuses on language and assigns probabilities to word sequences to generate text that mimics human language.

What distinguishes an LLM from standard language models is its scale — it is built with significantly larger datasets and requires more computing power and a greater number of parameters (the set of coefficients used in deep learning). Hence, the term ‘large’ refers to the volume of data input, computational workload, and number of parameters involved.

LLMs have gained attention for enabling AI to possess human-like language abilities. This once seemingly impossible feat is now achievable thanks to their capacity to perform natural language processing on a massive scale, enabling fluent conversations and high-precision language tasks. They are currently being deployed in various fields, including business and education.

Related terms and comparisons

LLM versus machine learning
Machine learning involves training machines to recognize patterns and rules from large volumes of data to make predictions and decisions. It encompasses a wide range of applications — not just natural language processing, but also image processing, image recognition, and speech recognition. LLMs are one specific application of machine learning. More precisely, deep learning and generative AI are subfields of machine learning, and LLMs fall under generative AI with a focus on language (text) data.

LLM versus generative AI
LLMs are a subset of generative AI. While LLMs focus on text-based tasks such as classification, summarization, question answering, and text generation based on given input, generative AI includes a broader scope — covering the generation of images, videos, audio, and more.

LLM versus natural language processing
Natural language processing (NLP) is a field within machine learning that focuses on processing human language. LLMs are models that fall under this domain.

As the name suggests, NLP refers to technologies that process natural human language, and LLMs use these techniques specifically to generate text. Therefore, LLMs are also often referred to as natural language generative AI.

Basic mechanism of LLMs

LLM algorithms

The operation of LLMs generally involves two key stages:

• Pre-training: Using large text datasets to train the model.

• Fine-tuning: Adjusting performance through task-specific refinement.

LLMs go through these stages to build capabilities. When given a prompt, such as a command or question, the model encodes the input and generates an appropriate response. To handle these processes efficiently, advanced GPUs and high-capacity storage systems capable of parallel processing are essential.

LLM capabilities

LLMs specialize in text generation and can perform a wide range of tasks previously considered exclusive to humans, such as:

• Answering questions

• Summarizing text

• Sentiment analysis

• Machine translation

• Keyword extraction

• Data consolidation

• Writing articles

• Debugging and generating code

These functions make LLMs a valuable tool for supporting and streamlining traditional workflows.

6 Application scenarios of LLMs

1. Chatbot systems
   Traditionally, customer inquiries were handled by phone, which required significant manpower and was difficult to scale to 24/7 service. Chatbot systems, powered by LLMs, automate responses and improve over time by learning from previous interactions. This enhances accuracy and better meets customer needs.

2. Application development
   Previously, programming was done manually by skilled developers, often resulting in high costs and inconsistent quality. LLMs can now automate code generation, testing, and debugging. However, effective use of LLMs for development depends heavily on prompt-engineering skills.

3. Content creation
   LLMs have become more useful in creating content such as articles and advertisements. For example, by prompting keywords or product names, LLMs can generate SEO-optimized outlines or taglines that align with the brand image.

4. Competitive-intelligence analysis
   Traditionally, analyzing competitors involved manual review of large volumes of text, which was time-consuming and subjective. LLMs can automatically analyze reviews, news, and public sentiment to extract insights on competitors’ strengths, weaknesses, and market trends, saving time and improving objectivity.

5. Internal data exploration
   Previously, internal data retrieval involved manual searches and filtering. With LLMs, users can simply enter relevant keywords to extract and summarize the needed information from large datasets, significantly reducing workload and improving decision-making.

6. Creating educational curricula
   LLMs are transforming education. Instead of teachers manually designing course plans, LLMs can help create teaching materials and assessments. By analyzing student performance and interest levels, LLMs can also suggest personalized learning plans tailored to each student.

Representative LLM models

Currently, various LLMs are used across the application scenarios described earlier. Below is a list of notable large language models:

* Transformer: a neural network that uses self-attention to understand context across long text.

Key considerations when using LLMs

LLMs are an innovative technology, but their use comes with certain risks and precautions. Here are three important points to understand before implementing LLMs:

1. Output-accuracy issues

The accuracy of LLM outputs depends heavily on the amount and quality of training data. As a result, depending on the type of question, the model may provide inaccurate or vague answers.

To address this, filtering mechanisms can be used to check LLM outputs and identify inappropriate content for correction or removal. However, these processes are often manual and can be time-consuming and labor-intensive.

2. AI hallucinations

LLMs can generate false yet seemingly plausible information, especially when trained on limited data — this is known as ‘hallucination’.
For example, if prompted to describe a specific person, the model might fabricate incorrect details, and users without relevant knowledge might mistakenly trust the response.
To mitigate this, users should be mindful of hallucinated content and refine prompts or discard misleading results.

3. Security risks

As LLM capabilities improve, the risk of malicious use also increases. Attackers may input harmful prompts to cause data leaks or generate inappropriate content. Specific risks include:

• Prompt injection: Manipulating the model with malicious inputs to generate misleading or false content.

• Prompt peaking: Extracting confidential prompts used during model training.

• Jailbreaking: Using adversarial prompts to bypass safety restrictions, such as pushing a neutral model to express extreme views.

While protective mechanisms are being developed, it’s crucial to have a basic understanding of these risks.
Additionally, since traditional CPUs can't handle the large-scale parallel processing needed for LLMs, selecting the right GPU is essential.

How to choose the right GPU

In general, newer GPUs offer better performance, but high-end models can be expensive. It's important to evaluate cost-effectiveness and choose the highest-spec GPU your budget allows. Key factors to consider include:

• Multiple GPUs: More GPUs mean higher performance, though this also increases power consumption.

• VRAM capacity: Higher VRAM allows more data to be processed, essential for large model operations.

• Cooling performance: Effective thermal management ensures sustained high performance.

• Power efficiency: High-performance GPUs often consume more power, so balance performance with energy use.

Boosting LLM accuracy: High-performance GPU recommendation

The accuracy of LLMs depends on both training data and the processing capabilities of the hardware.
For effective pre-training and inference, powerful GPUs are indispensable.

ASUS offers server systems powered by the high-performance NVIDIA GPU.
These servers combine top-tier AI computing with industry-leading graphics acceleration, making them ideal for generative AI, LLM inference/training, 3D graphics, rendering, and image processing — redefining workloads in next-generation data centers.

The servers also feature advanced cooling technologies, enabling high performance with excellent power efficiency.

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