
In Part 1 of this series, we created a quantized version of the Qwen3-8B model (Qwen3-8B-W8A8) using LLM-Compressor with 8-bit weight and activation quantization. In this notebook, we'll demonstrate how to deploy and compare this quantized model against its full precision counterpart (Qwen3-8B) using Vast.ai. We'll examine both deployment processes and compare the outputs to assess any quality differences between the models while highlighting the efficiency gains from quantization.

## Key benefits of quantized models:

- Reduced memory footprint (approximately 1.7x smaller in our case)
- Lower inference latency
- Decreased computational requirements
- More affordable deployment options

Let's start by exploring how to deploy our quantized model on Vast.ai.

## Deploying our Model on Vast.ai

First, we will install the Vast.ai SDK and input our API key. This allows us to interact with the Vast.ai platform programmatically and manage our GPU instances.

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

```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
```

Next, we'll search for an appropriate GPU instance to serve our quantized model. Since this is a W8A8 quantized version of Qwen3-8B, we can use a machine with less VRAM than would be required for the full model. We're looking for machines with at least 24GB of VRAM, which is sufficient for our 8-bit quantized model and its context window:

```bash
vastai search offers "compute_cap >= 750 \
geolocation=US \
gpu_ram >= 24 \
num_gpus = 1 \
static_ip = true \
direct_port_count >= 1 \
verified = true \
disk_space >= 20 \
rentable = true"
```

Once we've identified a suitable instance from the search results, we'll deploy our quantized model to that instance. We're using the vLLM Docker image with an OpenAI-compatible API, which makes it easy to serve and interact with our model.

Note that we're deploying the W8A8 quantized version of Qwen3-8B that was created and uploaded to Hugging Face in Part 1. Please replace `your-hf-username` with your actual Hugging Face username to use your compressed model:

```bash
export INSTANCE_ID= #insert instance ID
vastai create instance $INSTANCE_ID --image vllm/vllm-openai:latest --env '-p 8000:8000' --disk 20 --args --model your-hf-username/Qwen3-8B-W8A8
```

## Calling Our Model Using the OpenAI SDK

Now that our model is deployed and running on Vast.ai, we'll set up a client to interact with it. The vLLM server exposes an OpenAI-compatible API, allowing us to use the OpenAI SDK to send requests to our model.

First, we need to install the OpenAI SDK if you haven't already:

```bash
pip install openai
```

Then, we use the OpenAI SDK and the `VAST_IP_ADDRESS` and `VAST_PORT` from our instance to call our model.

Also add your hugginface username `HF_USERNAME` to call the model we saved to hugginface.

```python
from openai import OpenAI

VAST_IP_ADDRESS = "VAST_IP_ADDRESS"
VAST_PORT = "VAST_PORT"

HF_USERNAME="HF_USERNAME"

# Create a client instance pointing to the vLLM server
client = OpenAI(
    api_key="dummy-key",  # vLLM doesn't require a real API key
    base_url=f"http://{VAST_IP_ADDRESS}:{VAST_PORT}/v1"  # Point to your vLLM server
)

# Call the model
response = client.chat.completions.create(
    model=f"{HF_USERNAME}/Qwen3-8B-W8A8",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What are the major applications of deep learning?"}
    ],
    temperature=0.7,
    max_tokens=1000
)

# Print the response
print(response.choices[0].message.content)
```

Output:

```
<think>
Okay, the user is asking about the major applications of deep learning. I need to provide a comprehensive yet clear answer. Let me start by recalling the different areas where deep learning is commonly used.

First, computer vision comes to mind. That includes image recognition, object detection, and maybe even image generation. Then there's natural language processing, like chatbots, translation, and sentiment analysis.

I should also mention speech recognition, which is used in virtual assistants. Autonomous vehicles rely on deep learning for perception tasks. Healthcare is another big area, such as medical imaging analysis and drug discovery.

Finance uses deep learning for fraud detection and algorithmic trading. Recommender systems in e-commerce and streaming services are also important. Maybe touch on generative models like GANs and diffusion models for creating art or text.

Wait, I should categorize these applications to make it organized. Let me list them under different headings. Also, I need to ensure I don't miss any major areas. Oh, maybe mention robotics and control systems too.

I should check if there are any recent advancements or emerging fields. For example, deep learning in gaming, like reinforcement learning for game AI. Also, maybe something about anomaly detection in various industries.

Need to keep the explanation straightforward without too much jargon. Make sure each application is explained briefly but clearly. Avoid going into too much technical detail unless necessary.

Let me structure the answer with clear sections for each major application. Start with an introduction, then list each area with a brief description. Conclude by summarizing the versatility of deep learning.

Wait, the user might be looking for both current and potential future applications. Should I include that? Maybe a short note at the end about ongoing research and future possibilities.

Also, check for any overlaps or if some applications are better categorized under specific fields. For example, NLP and speech recognition are both related to language processing.

I think that's a solid structure. Now, I'll draft the answer accordingly, making sure it's informative and covers all key areas without being too verbose.
</think>

Deep learning, a subset of machine learning, has revolutionized numerous fields due to its ability to model complex patterns in data. Here are its major applications across various domains:

---

### **1. Computer Vision**
- **Image Recognition**: Identifying objects, scenes, and patterns in images (e.g., facial recognition, medical imaging analysis).
- **Object Detection**: Locating and classifying multiple objects in a single image (e.g., autonomous vehicles, surveillance systems).
- **Image Generation**: Creating new images using Generative Adversarial Networks (GANs) or diffusion models (e.g., AI art tools).
- **Video Analysis**: Action recognition, video summarization, and scene understanding.

---

### **2. Natural Language Processing (NLP)**
- **Text Generation**: Writing articles, stories, or code using models like GPT or BERT.
- **Translation**: Real-time language translation (e.g., Google Translate).
- **Sentiment Analysis**: Determining emotions in text (e.g., social media monitoring).
- **Chatbots & Virtual Assistants**: Interactive AI assistants (e.g., Siri, Alexa, customer service bots).

---

### **3. Speech Recognition & Synthesis**
- **Voice Assistants**: Converting speech to text (e.g., Alexa, Google Assistant).
- **Speech-to-Text**: Transcribing audio for accessibility or transcription services.
- **Text-to-Speech**: Synthesizing natural-sounding speech (e.g., audiobooks, virtual characters).

---

### **4. Autonomous Systems**
- **Self-Driving Cars**: Perceiving environments, navigating, and making real-time decisions.
- **Robotics**: Controlling robotic arms, navigation, and object manipulation.
- **Industrial Automation**: Predictive maintenance and quality control in manufacturing.

---

### **5. Healthcare**
- **Medical Imaging**: Diagnosing diseases (e.g., tumors in X-rays, MRIs) using CNNs.
- **Drug Discovery**: Accelerating the development of new medications through molecular modeling.
- **Personalized Medicine**: Predicting patient outcomes based on genetic data and medical history.

---

### **6. Finance**
- **Fraud Detection**: Identifying unusual transaction patterns.
- **Algorithmic Trading**: Predicting market trends and executing trades.
- **Risk Management**: Assessing credit risk and financial stability.

---

### **7. Recommender Systems**
- **E-commerce**: Personalized product recommendations (e.g., Amazon, Netflix).
- **Streaming Services**: Content suggestions based on user preferences.
- **Social Media**: Curating news feeds and trending topics.

---

### **8. Gaming & Entertainment**
- **Game AI**: Creating intelligent opponents or NPCs (e.g., AlphaGo, Dota 2 bots).
- **Content Creation**: Generating music, art, or video using deep learning models.

---
```

## Compare with full `Qwen/Qwen3-8B` Model

Now that we've seen the output from our quantized model, let's deploy and test the full precision version of the same model for comparison. This will help us evaluate any potential quality differences and understand the tradeoffs of quantization.

First, we need to find a more powerful machine with significantly more GPU memory. The full precision model requires approximately 3-4x more VRAM than its quantized counterpart. Let's search for instances with at least 80GB of VRAM:

```bash
vastai search offers "compute_cap >= 750 \
geolocation=US \
gpu_ram >= 80 \
num_gpus = 1 \
static_ip = true \
direct_port_count >= 1 \
verified = true \
disk_space >= 40 \
rentable = true"
```

Now we'll deploy the full precision model. Notice the differences in our configuration compared to the quantized model:

1. We're requiring more disk space (40GB vs 20GB)
2. We're using a much more powerful GPU with significantly more VRAM
3. We're deploying the original model from Qwen directly (Qwen/Qwen3-8B)

```bash
export INSTANCE_ID= #insert instance ID
vastai create instance $INSTANCE_ID --image vllm/vllm-openai:latest --env '-p 8000:8000' --disk 40 --args --model  Qwen/Qwen3-8B
```

With the full precision model deployed, let's query it with the same prompt we used for the quantized model to perform a direct comparison of their outputs:

```python

VAST_IP_ADDRESS = "VAST_IP_ADDRESS"
VAST_PORT = "VAST_PORT"

# Create a client instance pointing to the vLLM server
client = OpenAI(
    api_key="dummy-key",  # vLLM doesn't require a real API key
    base_url=f"http://{VAST_IP_ADDRESS}:{VAST_PORT}/v1"  # Point to your vLLM server
)

# Call the model
response = client.chat.completions.create(
    model="Qwen/Qwen3-8B",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What are the major applications of deep learning?"}
    ],
    temperature=0.7,
    max_tokens=1000
)

# Print the response
print(response.choices[0].message.content)
```

```
<think>
Okay, the user is asking about the major applications of deep learning. Let me start by recalling what I know. Deep learning is a subset of machine learning that uses neural networks with many layers. It's been really popular in various fields. I need to list the main areas where it's applied.

First, computer vision comes to mind. Image recognition, object detection, maybe even things like facial recognition. Then there's natural language processing, like chatbots, translation services, text generation. Speech recognition is another big one, like virtual assistants.

What else? Autonomous vehicles use deep learning for perception tasks, like identifying objects on the road. Healthcare is another area, maybe medical imaging analysis, predicting diseases. Then there's recommendation systems used by companies like Netflix or Amazon.

Wait, what about robotics? Deep reinforcement learning is used in robotics for control systems. Also, maybe gaming, like in AlphaGo. Financial services for fraud detection or algorithmic trading.

I should also mention some more specialized areas like generative models for creating images or music, and maybe something about NLP like sentiment analysis. Oh, and maybe mention that these applications are driven by the ability of deep learning to handle large amounts of data and complex patterns.

Wait, the user might be looking for a comprehensive list, so I should structure it into categories. Let me check if I missed any major areas. Maybe cybersecurity? Or maybe something like anomaly detection in various industries. Also, maybe mention the difference between deep learning and traditional machine learning in terms of application.

I need to make sure each application is clearly explained with examples. Let me organize them into sections. Computer Vision, NLP, Speech Recognition, Autonomous Vehicles, Healthcare, Recommendation Systems, Robotics, Gaming, Financial Services, Cybersecurity, Generative Models, and maybe others.

I should also note that deep learning is used in both supervised and unsupervised tasks. Oh, and maybe mention some specific models like CNNs for images, RNNs or Transformers for NLP. But the user might not need the technical details, just the applications.

Wait, the user might be a student or someone new to the field, so keeping it clear and concise is important. Avoid jargon where possible. Also, make sure to highlight the impact of each application.

Let me make sure I cover all major areas. Maybe add something about drug discovery in healthcare. Or maybe mention that deep learning is used in weather prediction or climate modeling.

Hmm, I think that's a good list. Let me structure it with bullet points and explanations for each application. Also, maybe conclude with a note on the versatility and impact of deep learning.
</think>

Deep learning, a subset of machine learning, has revolutionized numerous fields due to its ability to model complex patterns and process large datasets. Here are the **major applications** of deep learning:

---

### **1. Computer Vision**
- **Image Recognition**: Identifying objects, people, and scenes in images (e.g., Google Photos, medical imaging).
- **Object Detection**: Locating and classifying objects in real-time (e.g., autonomous vehicles, security cameras).
- **Facial Recognition**: Used in biometric authentication (e.g., smartphones, border control).
- **Image Generation**: Creating realistic images (e.g., GANs for art, style transfer).

---

### **2. Natural Language Processing (NLP)**
- **Text Generation**: Writing articles, stories, or code (e.g., chatbots, AI assistants like Siri).
- **Machine Translation**: Languages like Google Translate use neural networks for real-time translation.
- **Sentiment Analysis**: Analyzing emotions in text (e.g., social media monitoring, customer feedback).
- **Speech Recognition**: Converting speech to text (e.g., voice assistants, transcription services).

---

### **3. Autonomous Systems**
- **Self-Driving Cars**: Detecting pedestrians, traffic signs, and obstacles (e.g., Tesla Autopilot, Waymo).
- **Robotics**: Controlling robotic arms and navigation systems (e.g., industrial robots, drones).
- **Reinforcement Learning**: Training agents for tasks like game playing (e.g., AlphaGo, Dota 2).

---

### **4. Healthcare**
- **Medical Imaging**: Diagnosing diseases from X-rays, MRIs, or CT scans (e.g., cancer detection).
- **Drug Discovery**: Accelerating the development of new drugs by predicting molecular interactions.
- **Personalized Medicine**: Tailoring treatments based on patient data (e.g., genomics analysis).

---

### **5. Recommendation Systems**
- **E-commerce**: Product recommendations (e.g., Amazon, Netflix).
- **Streaming Services**: Content suggestions (e.g., Spotify, YouTube).
- **Social Media**: Curating news feeds (e.g., Facebook, Instagram).

---

### **6. Finance**
- **Fraud Detection**: Identifying unusual
```

## Conclusion: Comparing Model Outputs and Size Tradeoffs

After deploying and testing both the quantized model (W8A8) and the full precision model, we can make several key observations:

### Model Size and Resource Requirements

- **Full Precision Model (Qwen3-8B)**:

  - Required ~80GB+ VRAM GPU
  - Needed 40GB disk space
  - Model size: ~16GB (FP16)

- **Quantized Model (Qwen3-8B-W8A8)**:
  - Required only 24GB VRAM GPU (~3.3x less memory)
  - Needed only 20GB disk space
  - Model size: ~9.5GB (8-bit weights and activations)

### Output Quality Comparison

Looking at the outputs from both models, we can observe:

1. **Structural Similarity**: Both models organized their responses with similar categories (Computer Vision, NLP, Healthcare, etc.) and maintained good formatting with markdown.

2. **Content Quality**: Both models produced high-quality, relevant content with appropriate examples for each application domain. The specific details varied slightly, but the core information was consistent.

3. **Reasoning Process**: Both models showed similar thinking processes in their `<think>` sections, demonstrating that the quantized model retained the structured reasoning capabilities of the original model.

4. **Minimal Quality Degradation**: The quantized model showed negligible degradation in output quality compared to the full precision model, despite being significantly smaller.

### Key Takeaways

1. **Deployment Accessibility**: The W8A8 quantized model makes deployment much more accessible, requiring far less expensive hardware while maintaining output quality.

2. **Cost Efficiency**: Using the quantized model can significantly reduce cloud GPU costs by allowing deployment on less expensive hardware.

3. **Production Readiness**: 8-bit quantization appears to be production-ready for this type of model, offering an excellent balance between efficiency and performance.

4. **Use Case Fit**: For most general text generation and understanding tasks, the quantized model appears to be a suitable replacement for the full precision model.

This notebook demonstrates that weight and activation quantization (W8A8) using LLM Compressor is an effective approach for deploying large language models in resource-constrained environments without significant quality compromises.

Vast.ai provides an excellent platform for this workflow, allowing us to use high-end GPUs for model compression and more cost-effective GPUs for efficient inference.


Compare quantized vs. full Qwen3-8B models deployed on Vast.ai. See quality vs. efficiency tradeoffs using W8A8 compression with LLM-Compressor and vLLM.

Using LLM-Compressor to Quantize Qwen3-8B on Vast.ai (Part 2)


As AI models grow increasingly powerful, they're also becoming increasingly expensive to deploy. The latest language models require massive amounts of GPU memory and computational resources, putting them out of reach for many teams and use cases. Model compression has emerged as a critical technique for making these models more accessible while maintaining their performance.

Today, we'll explore how to compress large language models using LLM-Compressor and deploy them cost-effectively on Vast.ai. We'll walk through the complete process of taking a 16GB model and reducing it to approximately 9.5GB while preserving quality—making deployment significantly more affordable and accessible.

## The Tools: LLM-Compressor and Vast.ai

### LLM-Compressor: Making Models Leaner

The vLLM team has developed [LLM-Compressor](https://github.com/vllm-project/llm-compressor) as a comprehensive solution for reducing model size without sacrificing performance. Unlike other compression tools that focus on a single technique, LLM-Compressor offers a complete toolkit for optimization.

What makes it particularly powerful:

- **Comprehensive approach**: Combines quantization, pruning, and distillation techniques that can be mixed and matched
- **Production-ready**: Built specifically for deployment scenarios with seamless vLLM integration
- **Developer-friendly**: The one-shot API handles the complex compression pipeline automatically
- **Proven techniques**: Implements industry-standard methods like GPTQ, AWQ, and SmoothQuant

### Vast.ai: Affordable GPU Power

Traditional cloud providers can make GPU-intensive tasks prohibitively expensive. Vast.ai changes this equation by creating a marketplace where you can access high-end GPUs at competitive rates—often significantly cheaper than major cloud platforms.

For model compression workflows, Vast.ai offers several advantages:

- **Flexible pricing**: Pay-per-hour model means you only pay for compression time, not idle resources
- **Hardware variety**: Choose from different GPU types based on your specific memory and compute requirements
- **Quick deployment**: Get instances running in minutes without complex setup procedures
- **Community-driven**: Access to a wider range of hardware options through the marketplace model

## In this Tutorial

This tutorial demonstrates how to:

1. Compress the `Qwen3-8B` model using LLM-Compressor with 8-bit weight and activation quantization (W8A8)
2. Upload the compressed `Qwen3-8B-W8A8` model to Hugging Face for easy sharing and deployment

In the second part of this series, we'll show how to deploy the compressed `Qwen3-8B-W8A8` model on Vast.ai for efficient inference and compare its performance with the full-precision model.

## GPU Requirements

Model compression requires significant computational resources, especially for larger models. For compressing the Qwen3-8B model, we recommend the following specifications:

### Minimum Requirements:

- **GPU Memory**: 30GB+ VRAM (e.g., A5000, A6000, RTX 4090, V100-32GB)
- **GPU Compute**: CUDA-compatible GPU with compute capability 7.0+
- **System RAM**: 64GB recommended (minimum 32GB)
- **Storage**: 80GB free disk space for model files and datasets
- **CUDA Version**: 11.8 or newer

### Why these requirements?

- The full Qwen3-8B model requires approximately 16GB just to load in FP16 format
- During compression, we need memory for both the original model and compression overhead
- Calibration datasets and intermediate files require additional storage space
- Model compilers and quantization tools have their own memory overhead

For larger models (12B+), you would need even more VRAM (40GB+) or techniques like model sharding.

## Renting an Instance on Vast.ai

1. Ensure that you have a Vast.ai account. If not, sign up at [vast.ai](https://vast.ai)
2. Go to the Vast Templates in the Console https://cloud.vast.ai/templates/
3. Select the `PyTorch (CuDNN Runtime)` Template - this provides a pre-configured environment with PyTorch and CUDA
4. Filter for an instance with the following specifications for optimal performance:
   - 1 GPU with high VRAM (A5000, A6000, RTX 4090, or similar)
   - 30GB+ of VRAM (required for full model loading plus quantization overhead)
   - 80GB+ of storage (for model weights, checkpoints, and datasets)
   - 32GB+ of system RAM (64GB recommended for smoother operation)
5. Select an instance and click rent - hourly pricing will vary based on the selected hardware
6. Install the Vast TLS certificate in your browser to access the notebook server securely https://docs.vast.ai/instances/jupyter#1SmCz
7. Go to your Instances https://cloud.vast.ai/instances/ and click "Open" to access the Jupyter server on your instance
8. Upload this notebook to the server through the Jupyter interface

## Setup and Installation

First we will install and import our required packages. The cell below installs:

- Transformers: For loading and saving the model in Hugging Face format
- PyTorch: The deep learning framework that powers our model
- Hugging Face Hub: For uploading our compressed model
- LLM-Compressor: The toolkit we'll use for quantization

```bash
# Install required packages
# Using specific versions to ensure compatibility
pip install transformers==4.51.3  # Recent Transformers version with good Qwen3 support
pip install torch==2.7.0          # PyTorch version compatible with current CUDA drivers
pip install huggingface_hub       # Core Hugging Face Hub library
pip install huggingface_hub[hf_xet]  # Extension for handling large file transfers
pip install huggingface_hub[cli]     # Command-line interface for HF Hub
pip install llmcompressor            # The model compression toolkit
```

Next, import the necessary libraries:

```python
# Import necessary libraries
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.modifiers.quantization import GPTQModifier  # For weight quantization
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier  # For activation quantization preprocessing
from llmcompressor import oneshot  # Simplified API for end-to-end compression
```

## Define Model and Output Path

Next, we'll define our model variables and prepare for downloading the `Qwen/Qwen3-8B` model.

- `MODEL_ID`: The Hugging Face model ID for the original model
- `OUTPUT_MODEL_ID`: A name for our compressed model version
- `OUTPUT_DIR`: Local directory where the compressed model will be saved

```python
# Define model path and output directory
MODEL_ID = "Qwen/Qwen3-8B"  # Original model on Hugging Face
OUTPUT_MODEL_ID = "Qwen3-8B-W8A8"  # Our compressed model name
OUTPUT_DIR = "./Qwen3-8B-W8A8"  # Local directory for saving
os.makedirs(OUTPUT_DIR, exist_ok=True)  # Create output directory if it doesn't exist
```

Download the model files using the snapshot_download function:

```python
from huggingface_hub import snapshot_download

# Download the model files to local directory
# This is a more reliable way to download large models compared to the standard AutoModel approach
# This will download all model files (~16GB for Qwen3-8B) and may take some time depending on connection speed
snapshot_download(
    repo_id="Qwen/Qwen3-8B",
    local_dir_use_symlinks=False  # Set to True to use symlinks instead of copying files (saves space but may cause issues)
)
```

## Configure Quantization

Now we'll define our compression recipe, which consists of two steps:

1. **SmoothQuant**: A technique that redistributes the quantization difficulty between weights and activations, making activations easier to quantize with minimal accuracy loss

2. **GPTQ**: A weight quantization method specifically designed for large language models that uses a reconstruction approach to minimize the impact of quantization on model quality

We're using W8A8 quantization, which means:

- W8: Weights stored in 8-bit precision (instead of FP16/FP32)
- A8: Activations computed in 8-bit precision at inference time

This will reduce the model size by approximately 1.7x and potentially increase inference speed.

```python
# Create quantization recipe
# A recipe is a sequence of compression techniques applied in order
recipe = [
    # First apply SmoothQuant to make activations easier to quantize
    # The smoothing_strength parameter controls how aggressively to shift quantization difficulty
    # from activations to weights (higher = more shifting, 0.8 is a good balance)
    SmoothQuantModifier(smoothing_strength=0.8),

    # Then apply GPTQ for weight and activation quantization
    GPTQModifier(
        targets="Linear",  # Apply to all linear layers in the model
        scheme="W8A8",    # 8-bit weights and activations (other options: W4A16, W8A16, etc.)
        ignore=["lm_head"] # Don't quantize the language modeling head (important for output quality)
    )
]
```

## Apply Quantization with oneshot API

Now we'll apply our compression recipe to the model using the oneshot API.

The oneshot API handles the entire compression workflow:

1. Loading the model from Hugging Face or local path
2. Creating a calibration dataset (or using an existing one)
3. Running calibration to determine optimal quantization parameters
4. Applying the compression recipe
5. Saving the compressed model

We're using the `open_platypus` dataset for calibration, which consists of diverse open-source technical questions and answers. This helps ensure the model maintains accuracy across a range of technical topics.

**Note**: This cell will take a significant amount of time (30-60 minutes) and GPU memory to run.

```python
print(f"Starting compression of {MODEL_ID}...")

# Apply quantization using oneshot API
oneshot(
    model=MODEL_ID,               # HF model ID or local path to load the model from
    dataset="open_platypus",      # Calibration dataset - preset dataset with technical Q&A
    recipe=recipe,                # Our compression recipe defined above
    output_dir=OUTPUT_DIR,        # Where to save the compressed model
    max_seq_length=2048,          # Maximum sequence length for calibration samples
    num_calibration_samples=512,  # Number of samples to use for calibration (more is better but slower)
)
```

## Test the Compressed Model

After compression, it's important to verify that the model still performs reasonably well. This cell loads the compressed model and runs a simple inference test.

We'll evaluate the model on a prompt about AI technology to see how well it generates coherent and relevant text. This helps us verify that the quantization didn't significantly degrade model quality.

Note that a proper evaluation would involve more systematic testing across multiple prompts and metrics.

```python
# Load the compressed model from our output directory
tokenizer = AutoTokenizer.from_pretrained(OUTPUT_DIR)
model = AutoModelForCausalLM.from_pretrained(
    OUTPUT_DIR,
    device_map="auto",      # Automatically place model on available devices (CPU/GPU)
    torch_dtype="auto"      # Use the model's native precision (will be INT8 for quantized weights)
)

# Simple test to verify the model still works
prompt = "AI technology is transforming industries by"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate text with some randomness for creativity
with torch.no_grad():  # Disable gradient calculation for inference
    outputs = model.generate(
        inputs["input_ids"],
        max_new_tokens=100,     # Generate up to 100 new tokens
        do_sample=True,         # Use sampling instead of greedy decoding
        temperature=0.7         # Control randomness (lower = more deterministic)
    )

# Decode the generated text and print it
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Prompt: {prompt}")
print(f"Generated text: {generated_text}")
```

## Upload the Model to Hugging Face

Now that we have successfully compressed the model, we'll upload it to Hugging Face Hub for easy sharing and deployment.

First, we'll set up the necessary credentials and repository information:

```python
from huggingface_hub import HfApi, login
import os

# Define variables for Hugging Face upload
# You'll need to replace these with your actual Hugging Face information
HF_USERNAME = "HF_USERNAME" # Add your Hugging Face username here
HF_TOKEN = "HF_TOKEN"       # Replace with your actual Hugging Face token (from settings page)
HF_MODEL_ID = f"{HF_USERNAME}/{OUTPUT_MODEL_ID}"  # Full repository path on Hugging Face
```

Next, we'll create a proper model card (README.md) with information about our compressed model. This helps others understand what the model is, how it was compressed, and how to use it:

```python
# Create a detailed model card describing the compressed model
model_card_content = f"""---
language:
- en
- zh
library_name: transformers
license: other
datasets:
- open_platypus
---

# Qwen3-8B-W8A8

This is a compressed version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) using llm-compressor with the following optimizations:
- 8-bit weight quantization using GPTQ
- 8-bit activation quantization
- SmoothQuant pre-processing

## Model Details

- **Original Model**: Qwen/Qwen3-8B
- **Quantization Method**: GPTQ + SmoothQuant (W8A8)
- **Compression Libraries**: [llm-compressor](https://github.com/vllm-project/llm-compressor)
- **Calibration Dataset**: open_platypus (512 samples)
- **Optimized For**: Inference with vLLM
"""

# Write README.md to model directory
readme_path = os.path.join(OUTPUT_DIR, "README.md")
with open(readme_path, "w") as f:
    f.write(model_card_content)
```

Finally, we'll upload the compressed model to Hugging Face Hub. This process may take some time depending on your internet connection speed, as the model files are large (approximately 9.5GB even after compression):

```python
# Login to Hugging Face with your token
login(token=HF_TOKEN)

# Initialize the Hugging Face API
api = HfApi()

# Create the repository (if it doesn't exist)
api.create_repo(
    repo_id=HF_MODEL_ID,
    exist_ok=True,        # Don't error if repo already exists
    private=False,        # Set to True if you want a private repository
)

# Upload the model files
print(f"Starting upload of {OUTPUT_DIR} to {HF_MODEL_ID}...")
api.upload_folder(
    folder_path=OUTPUT_DIR,
    repo_id=HF_MODEL_ID,
    repo_type="model",
    ignore_patterns=["*.tmp", "*.log", "__pycache__"],  # Files to ignore during upload
    commit_message="Upload compressed Qwen3-8B with W8A8 quantization",
)

print(f"Model successfully uploaded to: https://huggingface.co/{HF_MODEL_ID}")
```

## Wrapping Up: From 16GB to 9.5GB Without Breaking the Bank

We've just walked through a transformation that would have seemed impossible a few years ago—taking a sophisticated language model and cutting its resource requirements by approximately 40% while preserving its capabilities.

### What We Accomplished

Our compression workflow delivered results across multiple dimensions:

**Technical achievements:**

- Reduced model footprint from 16GB to 9.5GB using W8A8 quantization
- Maintained output quality through careful calibration with technical datasets
- Created a production-ready model compatible with modern inference engines

**Economic impact:**

- Enabled deployment on more affordable GPUs with less VRAM requirements
- Reduced cloud inference costs through lower resource requirements
- Made advanced language models accessible to teams with limited budgets

**Operational benefits:**

- Faster model loading and reduced memory pressure during inference
- Better resource utilization allowing for higher throughput or multi-model serving
- Simplified deployment pipeline through automated compression workflows

### The Bigger Picture

Model compression offers an alternative approach to AI deployment. Rather than accepting ever-increasing resource requirements, we can actively optimize models for real-world constraints. The combination of LLM-Compressor's sophisticated algorithms and Vast.ai's accessible infrastructure makes this optimization practical for any team.

### Coming Up Next

In Part 2, we'll put our compressed model to the test. We'll deploy both our optimized Qwen3-8B-W8A8 model and the original full-precision version on Vast.ai, then conduct detailed comparisons of their performance, cost, and output quality. You'll see exactly how much you can save without compromising on results—and learn when compression makes the most sense for your specific use case.


Compress LLMs like Qwen3-8B with LLM-Compressor and deploy on Vast.ai. Cut model size ~40% while preserving quality—save cost, speed up inference, and scale smartly.

Posts about: Model Optimization

Using LLM-Compressor to Quantize Qwen3-8B on Vast.ai (Part 2)

Model Compression with LLM-Compressor and Deployment on Vast.ai (Part 1)