
Analyzing lengthy documents like SEC filings has traditionally required complex chunking strategies and careful management of context. However, with Mistral AI's [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) model and its 128k token context window, we can now process entire documents at once. This post demonstrates how to leverage this capability using Vast.ai's cloud GPU platform.

The Mistral Small 3.1 model represents a significant advancement in open-source language models. With 24 billion parameters, it combines state-of-the-art vision capabilities with an extended context window that makes it particularly suited for document analysis tasks.

Vast.ai's marketplace provides GPU configurations needed for optimal performance at cost-effective prices.

In this post, we will:
1. Set up a Vast.ai instance with the right GPU specifications
2. Deploy the model using vLLM for optimal serving performance
3. Utilize the model's native chat completions API
4. Leverage the long context window to analyze full public company annual reports (10-K filings)

This setup demonstrates how Mistral Small 3.1 with extended context windows can transform document analysis workflows, making it possible to process and understand complex documents in their entirety.

## Deploy Mistral Small 3.1 on Vast

Let's start by getting our environment set up. First, we need to install the Vast AI client and configure our access.

### Install Vast

Head over to the [Vast.ai Account Page](https://cloud.vast.ai/account/) to grab your API key. Once you have it, we'll use it to set up the client:

```bash
#In an environment of your choice
pip install --upgrade vastai
```

With the client installed, we can configure our API key:

```bash
# Here we will set our api key
export VAST_API_KEY="VAST_API_KEY" #Your key here
vastai set api-key $VAST_API_KEY
```

### Choosing the Right Hardware

Now we are going to search for GPUs on Vast.ai to run the Mistral-Small-3.1-24B-Instruct-2503 model. This model requires specific hardware capabilities to run efficiently with vLLM's optimizations. Here are our requirements:

1. A minimum of 60GB GPU RAM to accommodate:
   - Mistral model weights (24B Parameters)
   - KV Cache for handling the 128k token context window

2. A single GPU configuration, as Mistral-Small-3.1-24B-Instruct-2503 can be efficiently served on one GPU:
   Note: Multi-GPU configurations are supported if higher throughput is needed.

3. A static IP address for:
   - Stable API endpoint hosting
   - Consistent client connections
   - Reliable Langchain integration

4. At least one direct port that we can forward for:
   - vLLM's OpenAI-compatible API server
   - External access to the model endpoint
   - Secure request routing

5. At least 80GB of disk space to hold the model and other dependencies

We'll use `vast ai search offers` to find the right instance.

```bash
vastai search offers "compute_cap >= 750 \
gpu_ram >= 60 \
num_gpus = 1 \
static_ip = true \
direct_port_count >= 1 \
verified = true \
disk_space >= 80 \
rentable = true"
```

### Deploying the Server via Vast

Select a machine from the output above and copy its ID into the `INSTANCE_ID` variable below.

We will use `vastai create instance` to create an instance that:
1. Uses `vllm/vllm-openai:latest` docker image. This gives us an OpenAI-compatible server.
2. Forwards port `8000` to the outside of the container, which is the default OpenAI server port.
3. Forwards `--model mistralai/Mistral-Small-3.1-24B-Instruct-2503` to serve the Mistral model.
4. Uses Mistral-specific parameters:
   - `--tokenizer_mode mistral` to use the Mistral tokenizer.
   - `--config_format mistral` for the correct model config.
   - `--load_format mistral` to properly load the model weights.
5. Uses `--gpu-memory-utilization 0.95` to maximize context window.
6. Ensures that we have 80 GB of disk space.

Note: Ensure that you fill in your huggingface token `HUGGING_FACE_HUB_TOKEN` to access the model. You'll need to accept the terms on the model's huggingface page https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503.

```bash
export INSTANCE_ID= #insert instance ID
vastai create instance $INSTANCE_ID --image vllm/vllm-openai:latest --env '-p 8000:8000 -e HUGGING_FACE_HUB_TOKEN=HUGGING_FACE_HUB_TOKEN' --disk 80 --args --model mistralai/Mistral-Small-3.1-24B-Instruct-2503   --tokenizer_mode mistral   --config_format mistral   --load_format mistral   --gpu-memory-utilization 0.95
```

### Verify Setup and Get Instance IP Address and Port

Now, we need to verify that our setup is working. We first need to wait for our machine to download the image and the model and start serving. This will take a few minutes. The logs will show you when it's done. 

Next, go to the [Instances tab](https://cloud.vast.ai/instances/) in the Vast AI Console and find the instance you just created.

At the top of the instance, there is a button with an IP address in it. Click this and a panel will show up of the IP address and the forwarded ports. 
You should see something like: 
```
Open Ports
XX.XX.XXX.XX:YYYY -> 8000/tcp
``` 
Copy and paste the IP address (XX.XX.XXX.XX) into `VAST_IP_ADDRESS` and the port (YYYY) into `VAST_PORT` as inputs to the curl command below.

This curl command sends an OpenAI compatible request to your `vLLM` server. You should see the response if everything is set up correctly. 

Note: It may take a few minutes for the OpenAI server to initialize.

```bash
export VAST_IP_ADDRESS="VAST_IP_ADDRESS"
export VAST_PORT="VAST_PORT"
curl -X POST http://$VAST_IP_ADDRESS:$VAST_PORT/v1/completions -H "Content-Type: application/json"  -d '{"model" : "mistralai/Mistral-Small-3.1-24B-Instruct-2503", "prompt": "Hello, how are you?", "max_tokens": 50}'
```

## Set Up Our Model

### Setup Dependencies

First, we'll install the OpenAI SDK to communicate with our model on the Vast server.

```bash
pip install --upgrade openai
```

### Set up Our Model

Next, let's configure our model using the IP address and port from the Vast.ai Console. We'll create a simple function to interact with the model, using Mistral's recommended temperature of 0.15.

```python
from openai import OpenAI

# Your Vast.ai instance details
VAST_IP_ADDRESS = "VAST_IP_ADDRESS"
VAST_PORT = "VAST_PORT"

# Initialize the client with your server URL
client = OpenAI(
    api_key="EMPTY",  # vLLM doesn't require an actual API key
    base_url=f"http://{VAST_IP_ADDRESS}:{VAST_PORT}/v1"
)

# Simple function to chat with the model
def chat_with_model(prompt):
    response = client.chat.completions.create(
        model="mistralai/Mistral-Small-3.1-24B-Instruct-2503",
        messages=[
            {"role": "user", "content": prompt}
        ],
        temperature=0.15
    )
    return response.choices[0].message.content
```

#### Test our Model

Before we go on to long document analysis, let's test our function to make sure everything is working correctly.

```python
response = chat_with_model("what is the capital of France?")
print(response)
```

## Analyze Macy's Annual Reports to Track Store Numbers

The retail landscape has undergone significant changes, with major department stores closing across the US. Using our model's long context window, we'll analyze Macy's annual reports (10-K filings) to track their store count over the years.

### Download Filings

First, we need to download our data. We've gathered a list of Macy's annual report URLs from the SEC's EDGAR database (https://www.sec.gov/edgar/search/). Let's create a `macys_filings` list to store these URLs:

```python
import requests
from bs4 import BeautifulSoup
import re

# List of Macy's 10-K filing URLs
macys_filings = [
    "https://www.sec.gov/Archives/edgar/data/794367/000162828024012734/m-20240203.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000162828023009154/m-20230128.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000156459022011726/m-10k_20220129.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000156459021016119/m-10k_20210130.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000079436720000040/m-0201202010xk.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000079436719000038/m-0202201910xk.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000079436718000036/m-0203201810k.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000079436717000041/m-0128201710k.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000079436716000221/m-0130201610k.htm",
    "https://www.sec.gov/Archives/edgar/data/794367/000079436715000073/m-0131201510k.htm"
]
```

Next, we'll create a function that downloads and cleans the text from each filing, removing HTML formatting and other unnecessary elements:

```python
# Simple SEC headers
headers = {
    "User-Agent": "Your Name (your.email@example.com)",
    "Accept-Encoding": "gzip, deflate",
    "Host": "www.sec.gov"
}

def download_filing_text(url):
    """Download a filing and extract its text."""
    try:
        # Download the filing
        response = requests.get(url, headers=headers, timeout=30)
        response.raise_for_status()
        
        # Parse the HTML and extract text
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Remove script and style elements
        for script in soup(["script", "style"]):
            script.decompose()
        
        # Get all text with minimal processing
        text = soup.get_text(separator=' ')
        
        # Basic cleaning - collapse whitespace
        text = re.sub(r'\s+', ' ', text).strip()
        
        return text
        
    except Exception as e:
        print(f"Error downloading filing: {str(e)}")
        return ""
```

Now that we have our download function ready, let's use it to process all ten years of Macy's reports. We'll loop through each URL, download the content, and store the cleaned text:

```python
# Process all filings
filings_text = []

for i, url in enumerate(macys_filings):
    
    # Download and process filing
    print(f"Downloading {url} filing...")
    text = download_filing_text(url)
    
    if text:  # If we got text back (not empty string)
        print(f"✓ Successfully processed filing ({len(text)} characters)")
        filings_text.append(text)
    else:
        print(f"✗ Failed to process filing")

# Print summary
print("\n=== PROCESSING SUMMARY ===")
print(f"Successfully processed: {len(filings_text)}/{len(macys_filings)} filings")
```

### Extract Store Counts from Filings

Now comes the exciting part - analyzing the filings to track Macy's retail footprint over time. We'll use our model's long context window to examine each document in its entirety, asking a simple but powerful question: "How many stores did Macy's operate this fiscal year?" This approach demonstrates the advantage of processing complete documents at once - we don't need to worry about missing context or searching through different sections manually.

For each filing, we'll store the model's response in an `all_responses` list, building a comprehensive picture of Macy's store count evolution:

```python
all_responses = []
for i, text in enumerate(filings_text):
    print(f"\nProcessing file {i+1}/{len(filings_text)}")
    
    try:
        prompt = f"How many stores did Macy's operate this fiscal year? DATA: {text}"
        response = chat_with_model(prompt)
        all_responses.append(f"File {i+1} analysis: {response}")
        print(f"✓ File {i+1} processed successfully")

        print(response)
        
    except Exception as e:
        print(f"✗ Error processing file {i+1}: {str(e)}")
        all_responses.append(f"File {i+1} error: {str(e)}")
        
# Print all responses
print("\n=== ANALYSIS OF ALL FILES ===")
for response in all_responses:
    print(f"\n{response}\n")
```

The model's responses show detailed information extraction - accurately identifying store counts while providing precise dates (e.g., "February 3, 2024"), section references (e.g., "Item 1. Business. General"), and geographical context (e.g., "43 states, the District of Columbia, Puerto Rico and Guam") from each filing:

```
=== ANALYSIS OF ALL FILES ===

File 1 analysis: As of February 3, 2024, Macy's operated 718 store locations.


File 2 analysis: As of January 28, 2023, Macy's operated 722 store locations. This information is explicitly stated in the document under the section "Item 1. Business. General" where it mentions:

"As of January 28, 2023, the Company operated 722 store locations in 43 states, the District of Columbia, Puerto Rico and Guam."


File 3 analysis: As of January 29, 2022, Macy's operated 725 store locations. This information is explicitly stated in the "Item 2. Properties" section of the 10-K filing:

"As of January 29, 2022, the operations of the Company included 725 store locations in 43 states, the District of Columbia, Puerto Rico and Guam, comprising a total of approximately 112 million square feet."


File 4 analysis: Macy's, Inc. operated 727 store locations in 43 states, the District of Columbia, Puerto Rico, and Guam as of January 30, 2021.


File 5 analysis: As of February 1, 2020, Macy's operated 775 store locations in 43 states, the District of Columbia, Puerto Rico, and Guam.


File 6 analysis: As of February 2, 2019, Macy's operated a total of **867 stores**. This information is detailed in the 10-K filing under the section discussing the company's properties and store count activity.


File 7 analysis: As of February 3, 2018, Macy's operated approximately **852 stores** in 44 states, the District of Columbia, Puerto Rico, and Guam.


File 8 analysis: As of January 28, 2017, Macy's operated a total of **829 stores**. This number includes stores across various brands such as Macy's, Bloomingdale's, and Bluemercury, located in 45 states, the District of Columbia, Guam, and Puerto Rico.


File 9 analysis: As of January 30, 2016, Macy's operated a total of **870 stores**. This included Macy's, Macy's Backstage, Bloomingdale's, Bloomingdale's Outlet, and Bluemercury stores across 45 states, the District of Columbia, Guam, and Puerto Rico.


File 10 analysis: As of January 31, 2015, Macy's operated a total of 823 stores.
```

### Summarize Store Count Findings

Now that we have the store counts for each year, we will have Mistral summarize the results for us.

```python
prompt = "Give me a list of macys store numbers from 2015-2024 DATA: " + " ".join(all_responses)
response = chat_with_model(prompt)
```

```python
print(response)
```

We now have a concise summary of the number of stores that Macy's operated in each year.

```
Based on the provided data, here is the list of Macy's store numbers from 2015 to 2024:

- **2015:** 823 stores
- **2016:** 870 stores
- **2017:** 829 stores
- **2018:** 852 stores
- **2019:** 867 stores
- **2020:** 775 stores
- **2021:** 727 stores
- **2022:** 725 stores
- **2023:** 722 stores
- **2024:** 718 stores

This list reflects the number of store locations Macy's operated as of the specified dates each year.
```

## Conclusion

In this post, we demonstrated how Mistral Small 3.1's 128k token context window transforms SEC filing analysis. We:

- Deployed Mistral's 24B parameter model on a cost-effective Vast.ai GPU instance
- Processed complete 10-K filings without splitting them into smaller chunks
- Extracted store count data from various sections of Macy's annual reports
- Maintained the full document context throughout the analysis process

With this foundation in place, we can tackle more sophisticated document analysis tasks. While tracking store counts offers a simple demonstration, the real value lies in analyzing complex relationships across entire documents. Financial analysts can:

- Extract specific metrics while understanding their broader context
- Connect information from management discussions with financial data
- Assess risk factors in relation to company performance
- Identify patterns across different sections of a single document

This capability extends beyond financial analysis. The same approach works for legal contracts, academic papers, technical documentation, and any other domain where processing long documents as complete units provides better insights.

By combining Mistral's context window capabilities with Vast.ai's accessible GPU infrastructure, comprehensive document analysis becomes more practical and accessible for a wider range of users and use cases.


Deploy Mistral Small 3.1 on Vast.ai to analyze SEC filings with a 128k token context window, extracting insights like Macy’s store count trends from 2015-2024.

SEC Filing Analysis Using Mistral Small 3.1's Long Context Window


DeepSeek may be getting a [lot of hype](https://vast.ai/article/deepseek-r1-open-source-disruptor-or-overhyped-upstart) recently, but there's a new rival on the scene. Alibaba Cloud's Qwen series of AI models is setting a new standard for intelligence in the open-source world.

Earlier this year, Alibaba Cloud launched **Qwen 2.5-Max** – and it reportedly [outperforms](https://www.reuters.com/technology/artificial-intelligence/alibaba-releases-ai-model-it-claims-surpasses-deepseek-v3-2025-01-29/) other foundation models like DeepSeek-V3, GPT-4o, and Llama-3.1-405B "almost across the board," according to the company. Not only that, but it also [powered](https://www.scmp.com/tech/big-tech/article/3298233/alibabas-qwen-powers-top-10-open-source-models-china-ai-know-how-goes-beyond-deepseek) the top 10 open-source LLMs on Hugging Face's rankings last month. All of the top-ranked models on the Open LLM Leaderboard were trained and developed on the updated open-source versions of Qwen.

So what is Qwen, and what can you do with it? Let's take a closer look\!

### Qwen 2.5: Advancements and Features

The Qwen model series includes Qwen (an LLM), Qwen-VL (a vision-language model), Qwen-Audio, Qwen-Coder, and Qwen-Math. The latest Qwen 2.5 models have been pre-trained on a large-scale dataset of up to 18 trillion tokens, resulting in a substantial knowledge base as well as much-improved capabilities in coding and math.

In terms of performance metrics, Qwen 2.5 is quite impressive. It [achieved](https://www.alibabacloud.com/en/solutions/generative-ai/qwen) a score of 85+ on the Massive Multitask Language Understanding (MMLU) benchmark, a HumanEval score of 85+ in coding, and a MATH benchmark score above 80\.

Beyond these core competencies, Qwen 2.5 has highly advanced abilities in following instructions and understanding and generating structured data. It can handle diverse system prompts, demonstrating an adaptability that's well suited for role-based interactions and condition-setting for chatbots.

The models can even interact with software on PCs and mobile devices. One user posted a video online [showing](https://techcrunch.com/2025/01/27/alibabas-qwen-team-releases-ai-models-that-can-control-pcs-and-phones/) Qwen 2.5-VL opening the Booking.com app for Android and booking a flight from Chongqing to Beijing\!

### Specialized Models in the Qwen Family

Each open-source model in the Qwen series is designed for a specific domain. The following are the main options in the Qwen 2.5 lineup:

-   **Qwen 2.5-VL** – This large vision-language model processes images, text, and bounding boxes to recognize and analyze content. It can read text in Chinese and English, compare visuals, create stories, solve math problems, and answer questions.

-   **Qwen 2.5-Audio** – Designed to process audio and text, this audio language model accepts a variety of audio formats (including speech, music, and natural sounds) and generates text responses.

-   **Qwen 2.5-Max** – Pre-trained on over 20 trillion tokens and post-trained with curated Supervised Fine-Tuning (SFT), this high-performing Mixture-of-Expert (MoE) model has surpassed DeepSeek V3, Llama 3.1, and others in benchmarks like Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond.

-   **Qwen 2.5-Coder** – This coding model supports up to 128K tokens of context, covers 92 programming languages, and delivers competitive performance against larger language models in code generation, multi-language coding, completion, and repair.

-   **Qwen 2.5-Math** – Pre-trained and fine-tuned on synthesized data, this mathematical LLM supports English and Chinese queries and excels in Chain-of-Thought (CoT), Program-of-Thought (PoT), and Tool-Integrated Reasoning (TIR) while outperforming most 70B math models.

Furthermore, Qwen APIs enable users to develop generative AI applications for tasks like writing, image generation, and audio analysis, boosting efficiency and transforming the customer experience for businesses.

### Powering Qwen with Vast.ai

Given how competitive the Qwen models are with larger, more established AI systems – and the fact that they're open source – they offer an exciting opportunity for AI researchers, developers, and enthusiasts to experiment and innovate at minimal cost.

However, deploying the models on high-performance hardware is crucial for optimal performance. That's where Vast.ai comes in. Our platform makes this possible with **on-demand [RTX 5090 GPU](https://vast.ai/article/nvidia-geforce-rtx-5090-specs-everything-you-need-to-know) rental**, for instance, providing the power needed to run Qwen models efficiently without the overhead of expensive infrastructure.

With many other GPU options in our marketplace, you can choose the right balance of performance and cost for your specific needs. Get started with Vast today and experience the full potential of Qwen with high-performance GPUs at your fingertips\!


Alibaba Cloud’s Qwen 2.5 AI models outperform rivals like DeepSeek and GPT-4o, excelling in coding, math, and vision. Available open-source, powered by Vast.ai GPUs.

Alibaba's Qwen: An Open-Source AI Model that Surpasses DeepSeek?

NVIDIA’s RTX 5090 delivers cutting-edge AI and gaming performance, but launch issues, high costs, and stock shortages make it hard to get. Try it now on Vast.ai!

NVIDIA GeForce RTX 5090 Specs: Everything You Need to Know


The growth of AI is outpacing compute. Today, there's an unprecedented demand for computing power as interest in AI surges worldwide – and servers just can't keep up.

More advanced AI models require increasingly powerful hardware. **Without enough GPUs, these large-scale AI systems struggle to run, and also cannot improve.**

Even OpenAI's CEO, Sam Altman, has [admitted](https://techcrunch.com/2024/10/31/openai-ceo-sam-altman-says-lack-of-compute-is-delaying-the-companys-products/) that computing limitations are slowing down the company's ability to release new products. Microsoft's CFO, Amy Woods, [noted](https://www.microsoft.com/en-us/Investor/events/FY-2025/earnings-fy-2025-q1) last year that demand for AI "continues to be higher" than their "available capacity."

If industry giants like OpenAI and Microsoft are feeling the strain, smaller organizations face even steeper challenges. Without deep pockets or dedicated data centers, they often have no choice but to explore other options.

With GPUs in short supply and demand skyrocketing, many companies are turning to a more flexible, scalable solution: **GPU-as-a-service, or GPUaaS**.

## What Is GPU-as-a-Service?

Simply put, **GPUaaS is a cloud-based solution that provides on-demand access to high-performance GPUs.** It fills a gap in the AI industry by enabling customers to rent the compute power they need, scaling up or down as their workloads demand.

Big cloud providers like AWS, Google Cloud, and Azure offer GPU instances, but GPUaaS goes further. Platforms like our own here at Vast.ai tap into a decentralized network of idle GPUs from data centers, enterprises, and even individuals, making high-performance compute more affordable and widely available.

As a result, GPUaaS addresses a critical need: namely, that **AI infrastructure must be scalable, cost-efficient, and accessible to everyone.**

## Why GPUaaS Is the Key to Scalable AI Infrastructure

AI adoption shows no sign of slowing down – and with it comes fluctuating and unpredictable demand for computing power at any given time. Traditional centralized infrastructure isn't well suited to this type of volatility.

Relying on centralized GPU resources often leads to wasted potential and frustrating roadblocks, such as:

-   **High upfront costs** for purchasing and maintaining GPUs.
-   **Underutilized hardware** during slow periods, resulting in inefficiency and wasted costs.
-   **Bottlenecks** when demand spikes, slowing down innovation and delaying results.
-   **Limited access** to GPUs due to supply chain slowdowns and shortages, impacting AI scalability.

This is where GPU-as-a-Service offers a smarter solution. With GPUaaS, you can:

-   **Quickly scale AI workloads** to match demand without over-provisioning.
-   **Reduce costs** with a flexible pay-as-you-go model while eliminating the risks of heavy upfront investment.
-   **Access worldwide GPU resources**, minimizing latency and enhancing performance.
-   **Decrease energy consumption** by leveraging existing, idle GPUs instead of powering additional servers.

## GPUaaS: The Future of AI Compute is Flexible

As AI continues to advance, organizations that can efficiently scale their compute power will have a clear advantage. GPU-as-a-Service reduces the barriers of cost, accessibility, and scalability, offering an alternative to traditional infrastructure. With GPUaaS, you can tap into a global network of GPUs and ensure you always have the resources you need, when you need them.

So why wait? In a world where AI innovation moves fast, compute should never be the bottleneck. **Vast.ai makes enterprise-grade GPUs accessible and affordable through our decentralized marketplace, offering savings of 5-6X on GPU compute.** Whether you're training cutting-edge models or running large-scale inference, we provide the flexibility and efficiency to keep your AI projects moving forward.

Get started and harness the power of GPUaaS with Vast.ai today!

GPU shortages are slowing AI growth. GPU-as-a-Service (GPUaaS) offers a scalable, cost-effective solution for on-demand AI compute power.

GPU-as-a-Service: The Scalable Solution to AI's Compute Crisis


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.

GANs vs. LLMs: What You Need to Know


# DeepSeek R1: Open-Source Disruptor or Overhyped Upstart?

DeepSeek, a Chinese AI firm, has drawn attention in recent weeks for its release of DeepSeek R1 – a large language model (LLM) that aims to stand shoulder to shoulder with more established players. Its rapid rise has shaken up the AI race, with comparisons to OpenAI's o1 in particular.

## Try it For Yourself on Vast

Our [Ollama + WebUI](https://docs.vast.ai/ollama-webui) guide uses DeepSeek r1:70b as an example. Follow it to try out DeepSeek on our GPU cloud.

## The Disruptive Growth of DeepSeek

DeepSeek's impact isn't limited to industry insiders. It's already gaining massive popularity among everyday users. The DeepSeek mobile app, which provides a chatbot interface for DeepSeek R1, shot to the top of the Apple App Store charts within days of its release on January 20, 2025.

But the biggest reason DeepSeek is [making waves](https://techcrunch.com/2025/01/28/deepseek-everything-you-need-to-know-about-the-ai-chatbot-app/)? **The company has seemingly disrupted the prevailing wisdom that developing cutting-edge LLMs costs a ton of money.** The DeepSeek R1 LLM was developed at a fraction of the cost of comparable models from other companies – reportedly less than $6 million as opposed to hundreds of millions or even billions of dollars.

Another key factor setting it apart: **DeepSeek R1 is free and open source.** Researchers and developers can freely experiment with, modify, and deploy the model without the restrictions of proprietary alternatives.

The meteoric rise of the R1 LLM threw the U.S. financial markets into turmoil, prompting investors to rethink the valuation of companies like NVIDIA and reevaluate the AI prospects of tech giants like Microsoft and Meta. A significant stock market sell-off occurred on January 27, 2025.

Beyond its financial impact, though, what exactly is DeepSeek and how did it develop a model that's turning heads? Let's back up and take a closer look at this new player on the AI scene.

## What Is DeepSeek?

Based in Hangzhou, China, **DeepSeek is an AI development firm that focuses on creating open-source LLMs.** While it operates as an independent lab, DeepSeek is backed by High-Flyer Capital Management, a China-based quantitative hedge fund specializing in AI-driven financial strategies.

From the start, DeepSeek built its own data center clusters to train its generative AI models. U.S. export restrictions on advanced chips had an impact on operations, however. DeepSeek was forced to train its more recent models using NVIDIA H800 chips, a less powerful alternative to the H100 chips available to U.S. companies.

DeepSeek's first model was released in November 2023, and since then the firm has developed several different variations. But DeepSeek only catapulted to global prominence in January 2025, when its R1 model was released – along with a web interface, mobile application, and API access.

## DeepSeek R1: A Challenge to the Status Quo?

The DeepSeek R1 model focuses on logical inference, mathematical reasoning, and real-time problem-solving. Compared to other LLMs, **it uses a more efficient and sparse Mixture-of-Experts (MoE) architecture, where different parts of the model specialize in handling different tasks.** For every input, only a few "expert" neural networks are selected to process each piece of data.

What's really sparked buzz, however, is DeepSeek R1's ability to match performance with OpenAI's latest ChatGPT models across various tasks. So R1 is a more efficient model, developed much more cheaply, with impressive performance. It sounds almost too good to be true...

How was it trained?

According to DeepSeek, the R1 model leveraged the following training innovations:

-   **Reinforcement Learning** – Using a large-scale reinforcement learning approach without supervised fine-tuning, DeepSeek employed techniques like group relative policy optimization (GRPO) to enhance reasoning capabilities.
-   **Reward Engineering** – Instead of relying on traditional neural reward models, DeepSeek created a rule-based reward system that delivers superior results (i.e., by structuring incentives to guide the model's learning during training).
-   **Emergent Behavior Network** – DeepSeek discovered that advanced reasoning patterns can emerge on their own through reinforcement learning, without being explicitly programmed.
-   **Distillation** – DeepSeek used efficient knowledge transfer techniques and distilled knowledge from larger, more complex AI models, allowing R1 to achieve strong performance with reduced computational demands.

Not all of this is new. DeepSeek does present some algorithmic and architectural novelties – such as the sparse MoE design and the use of reinforcement learning to refine synthetic data selection and optimize reasoning pathways during training – but the company isn't necessarily the first to implement these techniques.

Still, it has combined them in a way that makes its R1 model notably efficient and competitive.

## Too Good to Be True?

Unfortunately, there is a catch. **DeepSeek R1 reportedly [relied](https://www.theverge.com/news/601195/openai-evidence-deepseek-distillation-ai-data) on synthetic data from OpenAI's GPT-4o for its training**, pushing some of the computational burden onto the latter, much less efficient model.

So while R1 is touted as being highly efficient, those claims only apply to the model itself, not to the entire process used to train it. The R1 model's lean MoE architecture means it is indeed efficient when running inference, but its training pipeline requires external synthetic data generated by other models like GPT-4o – and that external data generation is computationally expensive.

This means that a significant part of the total AI development cost and compute burden is still being offloaded onto other, more complex models. **It's a cost-saving move for DeepSeek, but it doesn't negate the need for massive specialized compute in the industry overall.**

Time will tell how sustainable DeepSeek's approach is – and what consequences it may have for the company as scrutiny over training methods increases and competition in the AI space continues to grow.

...There may even be national security concerns, as the U.S. government [eyes](https://www.cbsnews.com/news/deepseek-ai-raises-national-security-concerns-trump/) the potential for harmful foreign influence. But that's a topic for another day.

## The Bottom Line on DeepSeek R1

Regardless of how one feels about it, DeepSeek R1 has certainly made a bold entrance into the world. While its efficiency claims come with important caveats, the model remains a fascinating case study in how AI development is evolving.

Plus, its open-source license allows for commercial use, which is a pretty major draw for developers around the world.

## Want to Learn More

DeepSeek R1 is a **massive** model that will not fit on a single 8xH200 instance unless you run a quantized version. The 4bit quantization will run on a 8xH100 or 4xH200 node. The sweet spot might be the 8bit quantized model. If you are interested in trying it out, [reach out to us](https://vast.ai/contact-sales) so we can talk to you about your use case and help get it setup.

As always, we'd be happy to assist you in any way we can! We're here to support your AI experiments, providing the compute power you need to explore, test, and innovate – every day.


DeepSeek R1: Open-source LLM shaking up the AI race. Developed for under $6M, it rivals ChatGPT's performance. Try it on Vast's GPU cloud.

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