
You wouldn't buy a car without taking it for a test drive, so why deploy a large language model (LLM) without benchmarking it first?

When you're running LLMs at scale, you need more than just theoretical performance data. You're going to want to know how they actually perform under real conditions – throughput, latency, and hardware efficiency all matter. Skipping this step can result in higher costs and wasted resources.

Benchmarking early helps you avoid inefficiencies down the line, and it shouldn't take hours to get started. That's where **vLLM** comes in.

### A Smarter Way to Serve and Benchmark LLMs

vLLM is an open-source library that optimizes the inference and serving of LLMs, and it [simplifies](https://blog.vllm.ai/2023/06/20/vllm.html) the benchmarking process with an efficient architecture built around the attention algorithm PagedAttention. This mechanism reduces memory waste and improves throughput by storing attention data non-contiguously.

When fast and efficient LLM inference is crucial, understanding your model's performance starts with benchmarking. With vLLM, you can spin up a high-performance inference server and start collecting those benchmarks in minutes.

To make things even easier, you don't need to spin up your own infrastructure from scratch. Vast.ai provides an ideal platform to leverage vLLM, offering high-performance GPUs at a fraction of the cost of traditional cloud providers. With Vast, it's easy to find the right hardware for your model, and with simple Docker integration, you can get up and running with vLLM quickly.

This guide demonstrates how to benchmark any LLM using vLLM on Vast.ai, in just a few minutes.

### Getting Started: Benchmarking LLMs with vLLM

This guide walks you through how to **serve a large language model using vLLM** and run a benchmark test to evaluate performance. It’s using a **H100** **GPU server from [Vast.ai](https://vast.ai/)** with the **"PyTorch" template**. In this example, we’ll use the model `meta-llama/Llama-3.1-8B-Instruct`, but you can use **any Hugging Face-supported model** compatible with vLLM.

**1\. Install required Python packages**

First, install the **vLLM** library along with **Pandas** and Hugging Face’s **datasets** library. For example, you can use pip in your Python environment:

```shell
pip install vllm pandas datasets
```

This installs vLLM and the other libraries for data handling.

**2\. Log in to Hugging Face**

To access the Meta Llama 3.1 model, you need a Hugging Face API token. Then run:

```shell
huggingface-cli login
```

This will prompt you to paste your Hugging Face **access token** (from your Hugging Face account settings). After entering the token, the CLI saves it so vLLM and other tools can download the model.

## **3\. Start the vLLM server**

Now start the vLLM server hosting the Llama 3.1 8B Instruct model. vLLM’s OpenAI-compatible server listens on localhost port 8000 by default. Use the `vllm serve` command with **nohup** to run it in the background and redirect output to a log file. For example:

```shell
nohup vllm serve meta-llama/Llama-3.1-8B-Instruct > vllm.log 2>&1 &
```

This command launches the server with the specified model and writes all output (including any Uvicorn logs) to `vllm.log`. The server will run in the background. (The vLLM docs give a similar example: `vllm serve <model-name>`) By default, this serves the model on [`http://localhost:8000`](http://localhost:8000).

## **4\. Test the server with a curl request**

Once the server is running, you can test it by sending a chat completion request to the API. For example, use `curl` to call the chat endpoint (`/v1/chat/completions`) with a simple prompt:

```shell
curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
          "model": "meta-llama/Llama-3.1-8B-Instruct",
          "messages": [
              {"role": "system", "content": "You are a helpful assistant."},
              {"role": "user", "content": "What is the capital of France?"}
          ]
        }'
```

This sends a JSON payload to the server. The `model` field names our loaded model, and `messages` contains a chat-style conversation. The server will respond with a JSON completion. (The official vLLM docs show a similar curl example for chat completions)

You should see a JSON response in the terminal with the model’s answer.

## **5\. Clone the vLLM repository**

To access the benchmark script, clone the vLLM source repository:

```shell
git clone https://github.com/vllm-project/vllm.git
cd vllm/benchmarks
```

This pulls the latest vLLM code. Now you have the `benchmark_serving.py` script and related files in the `benchmarks` folder.

## **6\. Run the benchmark script**

Finally, run the built-in benchmark script to measure serving performance. The `benchmark_serving.py` tool sends many requests to the running server and reports throughput/latency. For example, you might run:

```shell
  python3 vllm/benchmarks/benchmark_serving.py \
    --backend vllm \
    --base-url http://127.0.0.1:8000 \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --tokenizer meta-llama/Llama-3.1-8B-Instruct \
    --dataset-name random \
    --random-input-len 128 \
    --random-output-len 128 \
    --num-prompts 80 \
    --max-concurrency 4 \
    --temperature 0.7

```

-   `--backend vllm`: Use the vLLM backend to send benchmark requests.

-   `--base-url http://127.0.0.1:8000`: Address of your running vLLM server.

-   `--model meta-llama/Llama-3.1-8B-Instruct`: Name of the model being benchmarked.

-   `--tokenizer meta-llama/Llama-3.1-8B-Instruct`: Tokenizer used for encoding/decoding; usually the same as the model.

-   `--dataset-name random`: Use synthetic/randomly generated prompts instead of a real dataset.

-   `--random-input-len 128`: Length (in tokens) of each generated input prompt.

-   `--random-output-len 128`: Desired length (in tokens) of the model's output.

-   `--num-prompts 80`: Total number of prompts to send during the test.

-   `--max-concurrency 4`: Number of prompts sent in parallel (simulates batch size).

-   `--temperature 0.7`: Controls randomness in generation; higher \= more creative.

This example command (modeled on published vLLM benchmarks) will run 80 chat requests against your server with concurrency 4\. After it finishes, the script prints stats like throughput and latency.

By following these steps and citing vLLM’s documentation and examples, you can serve `Llama-3.1-8B-Instruct` with vLLM and evaluate its performance. Adjust parameters (batch size, number of tokens, etc.) in the commands above as needed for your environment.

### Conclusion

And that's it\! A fast, easy-to-use LLM benchmarking workflow powered by vLLM. In just ten minutes, you can gather meaningful performance data that will help you make smarter decisions about which models (and hardware) are right for your specific project and workload.

Need powerful GPUs to run your tests? Vast.ai offers on-demand access to H100s, RTX 5090s, and other high-performance options at a fraction of the typical price – so you can save 5-6X on GPU compute. Get started today and benchmark on your terms\!


Benchmark LLMs in minutes using vLLM on Vast.ai. Serve models like Llama 3.1-8B, test performance, and optimize cost with fast, efficient, real-world insights.

How to Benchmark An LLM with vLLM in 10 Minutes


Artificial intelligence has brought us a wide range of groundbreaking technologies. Two that have been particularly influential are **Generative Adversarial Networks** (GANs) and **Large Language Models** (LLMs).

To put it simply, GANs excel at generating realistic visual content – such as pictures and videos – by training neural networks to create and critique images, while LLMs dominate in text-based tasks with their ability to understand, generate, and manipulate human-like text across a vast array of domains and applications.

Understanding what each of these AI models can do – and knowing when to use a GAN, an LLM, or even both – can be key to making the most of your AI projects. Let's explore how they work, their differences, and the best ways to apply them to your specific needs!

## What Is a GAN?

### Understanding Generative Adversarial Networks

A **Generative Adversarial Network**, or GAN, is a machine learning model made up of two competing neural networks – a **generator** and a **discriminator**. They work together to create new data that closely resembles real-world examples. For instance, GANs can generate highly realistic images by learning from a dataset of actual images.

The term "adversarial" here is fitting. The generator's job is to create synthetic data, while the discriminator evaluates it, trying to distinguish between what's real and what's generated. As they continuously challenge each other, the generator gets better at producing increasingly realistic outputs – whether images, audio, or other types of data.

### Strengths of GANs

First and foremost, GANs dominate in their **ability to generate highly realistic synthetic content**. Unlike other generative models that rely on statistical approximations, the unique competitive training process used by GANs produces outputs that more closely resemble real-world data.

GANs are primarily used for **content creation and augmentation** – for instance, creating photorealistic faces, generating artistic styles based on existing artwork, and enhancing low-resolution images. They're remarkably well suited for creative endeavors that still call for realism and precision.

Unlike diffusion models, which generate content through a gradual, probabilistic denoising process with limited direct manipulation, GANs offer **more granular control**, allowing designers and researchers to intervene at specific stages of image generation. This means users can more directly guide the output by adjusting latent spaces, introducing constraints, or selectively modifying generated elements.

Also, because GANs only need a single forward pass in order to generate a sample, they **often perform faster than other models** despite their computational demands. This speed can be especially useful in applications where rapid prototyping or iterative design processes are required.

While GANs are best known for generating visual content, they can also **produce other data types, such as text, audio, and 3D models**. This versatility allows them to be applied across a wide range of industries and technical fields.

Another advantage of GANs is the role they can play in **data augmentation and enhancement.** For instance, in scenarios where real-world data is limited or sensitive, GANs can generate synthetic datasets to train machine learning models more effectively. This allows GANs to **preserve privacy** (in cases where the generator has limited or no access to real-world data), **enhance model performance**, and **enable experimentation** without the constraints of traditional data collection.

### Example Use Cases for GANs

As mentioned, GANs are widely used for generating and enhancing synthetic data. Here are some examples of use cases for GANs:

-   Image generation and enhancement, especially photorealistic faces
-   Augmenting datasets for machine learning tasks
-   Creating synthetic medical images for research
-   Style transfer and artistic design
-   Video generation and editing
-   3D model generation

From medical research to artistic expression, GANs serve as a practical solution for a variety of technical and creative applications.

And that brings us to large language models...

## What Is an LLM?

### Breaking Down Large Language Models

A **Large Language Model**, or an LLM, is an AI system that's been trained on massive amounts of text data, giving it the ability to understand, generate, and engage with human language in a natural way. It works by analyzing patterns and relationships in text using deep learning algorithms.

At the core of an LLM is a **transformer architecture** that leverages **self-attention mechanisms** to process text efficiently. These mechanisms allow the model to weigh the importance of different words within a sentence – regardless of their position – enabling it to capture long-range dependencies, contextual meanings, and subtle nuances in language.

This ability to consider context at multiple levels simultaneously is what makes LLMs highly effective at tasks like summarization, translation, paraphrasing, and human-like conversation.

### Strengths of LLMs

As we just touched on, the biggest strength of an LLM is its **deep contextual understanding of language**. It goes beyond the keyword matching and predefined rules of simpler language processing tools. Instead, LLMs are able to grasp the nuances, intent, and context behind words and phrases. They can process vast amounts of information and provide meaningful output that aligns with the intended context.

**Multi-task text processing**, in particular, encapsulates an LLM's deep, generalized understanding of language. It refers to the ability to perform multiple different language-related tasks without being specifically trained or retrained for each one individually.

For instance, an LLM can summarize and paraphrase text, translate between languages, analyze sentiment, correct grammar, expand on existing text, code in various programming languages, and generate different writing styles – all using the same underlying neural network, and often **performing multiple tasks within the same interaction**.

Another key capability of LLMs is their **scalability and adaptability**. Once trained, these models can be fine-tuned for specific applications. Businesses and developers can take pre-trained LLMs and refine them with industry-specific or internal data to enhance performance and accuracy for specialized tasks.

Additionally, LLMs can scale efficiently across different platforms and workflows. Their **ability to learn and evolve** with additional data ensures they remain relevant and effective over the long term.

### Example Use Cases for LLMs

LLMs can automate routine tasks or enhance complex workflows across a wide range of industries. The following are some areas where LLMs are used today:

-   Automated content creation and text editing
-   Code generation and review
-   Data analysis and summarization
-   Language translation that preserves nuance
-   Customer service chatbots and virtual assistants
-   Personalized learning and tutoring
-   Brainstorming and idea development

These use cases highlight just a few of the many ways LLMs can streamline processes, improve efficiency, and drive innovation.

## Key Differences Between GANs and LLMs

Now that we've explored both GANs and LLMs, let's look at how they directly compare to each other. Here's a side-by-side comparison for quick reference:

| Characteristic             | Generative Adversarial Networks (GANs)   | Large Language Models (LLMs)            |
| ---------------------------| ---------------------------------------- | --------------------------------------- |
| **Primary Output**         | Visual content (images, videos, audio)               | Textual content (text, code, etc.)               |
| **Core Mechanism**         | Two competing neural networks (generator and discriminator)                | Transformer architecture with self-attention mechanisms                | 
| **Training Approach**      | Adversarial approach with constant competition              | Supervised learning on massive text datasets             |
| **Key Strengths**          | - Highly realistic image generation <br>- Creative visual synthesis  <br>- Style transfer and manipulation | - Complex text understanding <br>- Contextual language generation <br>- Multi-task text processing |
| **Data for Training**      | Images, audio, structured data          | Text-based datasets           |
| **Compute Requirements**   | High GPU demand for training and image generation                 | Substantial computational resources for training and inference      |
| **Typical Applications**   | Digital art creation; medical imaging; face generation; fashion and design                 | Conversational AI; content writing; translation; code generation; research assistance                |

Next comes the question of which one you should use for your specific project...

## When to Use GANs vs. LLMs (Or Both)?

Choosing the right AI model for your needs is fairly straightforward. Here's a general guide:

**Use GANs when...**

-   Your goal is to create high-fidelity visual or audio content with precise control.
-   Realistic synthetic data is required for training machine learning models.
-   You need to enhance or modify existing images or videos.
-   Developing realistic simulations in fields like medical imaging.

**Use LLMs when...**

-   Generating, analyzing, or transforming textual content.
-   Creating intelligent conversational interfaces like chatbots.
-   Automating complex language-based workflows.
-   Extracting insights from large text collections.

**Combine GANs and LLMs for advanced applications that require...**

-   Integrated multimodal content generation.
-   Synthetic data generation that includes both visual and textual components.
-   Automated media generation with textual descriptions for context.

## Running GANs and LLMs on Vast.ai

At Vast.ai, you can access affordable GPU resources to train, fine-tune, experiment with, and deploy both GANs and LLMs at scale. Our platform offers a wide range of GPU options to match the demands of different workloads, so you can focus on training and deploying your AI models without worrying about infrastructure limitations.

We're proud to be the market leader in low-cost cloud GPU rental – whatever your needs are, we're here to help! Accelerate your AI projects with Vast today.

GANs generate realistic images, while LLMs excel in text-based tasks. Learn their differences, strengths, and how to choose the right AI model for your project.

Posts about: Llms

How to Benchmark An LLM with vLLM in 10 Minutes

GANs vs. LLMs: What You Need to Know