Serving Fine-Tuned Models with vLLM and TGI

In the previous lesson, we learned how to compress our model (Quantization). Now, we need an engine to run it.

If you use a basic Python script to answer user requests, your model will be slow. It can only handle one person at a time, and it wastes a lot of GPU power. Professional engineers use Inference Engines. These are specialized servers that "Optimizing the path" between the GPU and the user.

Currently, the two undisputed kings of AI serving are vLLM and TGI (Text Generation Inference).

1. vLLM: The PagedAttention Revolution

vLLM (Virtual Large Language Model) is currently the most popular open-source inference engine.

The Secret: PagedAttention.
The Legend: In traditional serving, the model reserves a giant block of VRAM for every user's "memory" (KV Cache). Most of that memory is wasted.
The vLLM Fix: vLLM treats VRAM like a computer treats RAM—it chunks it into small "Pages." It only uses exactly what it needs for each user.
The Result: You can serve $24 \times$ more users on the same GPU compared to standard Hugging Face code.

2. TGI (Text Generation Inference)

TGI was built by Hugging Face to power their "Inference Endpoints."

Best For: Stability and production-grade features like Continuous Batching.
Continuous Batching: In traditional batching, the model waits for everyone in the batch to finish before starting a new round. In Continuous Batching, as soon as one user's sentence is done, a new user is "slotted in" mid-process.
Pros: Excellent support for advanced quantization formats (like AWQ) out of the box.

Visualizing Throughput Gains

graph LR
    A["Standard Python Inference"] --> B["Throughput: 1 req/sec"]
    C["vLLM / TGI Engine"] --> D["Throughput: 20+ req/sec"]
    
    subgraph "The Optimization Gap"
    D
    end
    
    style D fill:#6f6,stroke:#333

3. Implementation: Launching a vLLM Server

One of the best things about vLLM is that it mimics the OpenAI API. This means any code you wrote for ChatGPT will work with your fine-tuned model instantly.

Command Line:

python -m vllm.entrypoints.openai.api_server \
    --model /path/to/your/fine-tuned-model \
    --quantization awq \
    --port 8000

Python Client (Usage):

import openai

client = openai.OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY" # vLLM doesn't need a key locally
)

response = client.chat.completions.create(
    model="your-model-name",
    messages=[{"role": "user", "content": "How are you?"}]
)
print(response.choices[0].message.content)

4. Why Engines Matter for Your ROI

If you use vLLM, you can host your model on a single A100 GPU and handle hundreds of concurrent users. If you use a basic script, you might need 10 A100s to handle the same traffic. Using a high-performance engine is the single best way to lower your AI infra costs.

Summary and Key Takeaways

PagedAttention is the core innovation of vLLM that prevents VRAM waste.
Continuous Batching allows for maximum GPU utilization without waiting for slow users.
OpenAI Compatibility: Both vLLM and TGI allow you to serve your model via the standard OpenAI API format.
Throughput: Moving to a professional engine can increase your capacity by $10x - 20x$.

In the next lesson, we will look at a specialized architecture for multiple models: Multi-LoRA Serving: One Base Model, Ten Adapters.

Reflection Exercise

If you are a startup with 5 users, do you need vLLM? What if you have 50,000 users?
Why does "Continuous Batching" help with user latency? (Hint: Does the user have to wait for the 'Batch' to fill up before they get their first token?)

SEO Metadata & Keywords

Focus Keywords: vLLM pagedattention explained, text generation inference TGI tutorial, serving fine-tuned LLM vLLM, continuous batching vs static batching, low latency AI serving. Meta Description: Scale your intelligence. Learn how to use vLLM and TGI to serve your fine-tuned models at 20x higher throughput, leveraging PagedAttention and continuous batching.