vLLM vs TGI: Which Inference Engine is Right for You?

Large language models have moved from research labs into production systems, and serving them efficiently has become a critical engineering challenge. Two inference engines dominate the open-source landscape today: vLLM, developed by UC Berkeley, and TGI (Text Generation Inference), developed by Hugging Face. Both promise high-throughput, low-latency LLM serving, but they take different architectural approaches and excel in different scenarios. This tutorial breaks down what each engine offers, how they compare, and how to choose between them for your specific workload.

What Is vLLM?

vLLM is a high-throughput inference engine built around a novel memory management technique called PagedAttention. Traditional LLM serving wastes a significant amount of GPU memory by pre-allocating contiguous blocks for the KV cache based on the maximum sequence length. PagedAttention, inspired by operating system virtual memory and paging, breaks the KV cache into smaller fixed-size blocks that can be allocated and freed dynamically. This dramatically reduces memory fragmentation and allows vLLM to pack many more concurrent requests into the same GPU.

The result is impressive throughput numbers — often 2x to 4x higher than naive implementations — while maintaining low latency. vLLM also supports continuous batching, tensor parallelism for multi-GPU setups, and a drop-in OpenAI-compatible API server.

What Is TGI?

Text Generation Inference is Hugging Face's production-grade inference server. It was built to power the Hugging Face Inference Endpoints service and is battle-tested at scale. TGI also uses continuous batching and supports tensor parallelism, but its distinguishing features lie elsewhere. TGI ships with built-in support for quantization (bitsandbytes, GPT-Q, AWQ), fine-grained token streaming via Server-Sent Events, and deep integration with the Hugging Face ecosystem — including automatic model downloading, safetensors loading, and compatibility with the Transformers library.

TGI also includes optimized kernels for specific architectures and supports features like watermarking, grammar-guided generation, and structured output through JSON schemas.

Why the Choice Matters

Selecting the wrong inference engine can cost you real money and performance. If you run a high-traffic API where hundreds of users send short prompts simultaneously, throughput and memory efficiency are paramount — and vLLM's PagedAttention gives it a strong edge. If you need to deploy a diverse zoo of models with different quantization formats, stream tokens to a chat UI, and integrate tightly with the Hugging Face Hub, TGI's ecosystem advantages may outweigh raw throughput differences.

The choice also affects operational complexity. Both engines are containerized and relatively easy to deploy, but their configuration surfaces, monitoring capabilities, and failure modes differ. Understanding these trade-offs before committing to an engine saves painful migrations later.

Key Feature Comparison

Memory Management: vLLM uses PagedAttention for efficient KV cache allocation. TGI uses a more traditional approach with continuous batching.
Throughput: vLLM generally achieves higher throughput on concurrent short-to-medium length requests due to PagedAttention.
Quantization Support: TGI has broader out-of-the-box support for bitsandbytes, GPT-Q, AWQ, and EETQ. vLLM supports AWQ, GPTQ, SqueezeLLM, and FP8 but with different coverage across model families.
Streaming: Both support token streaming. TGI uses Server-Sent Events with rich metadata. vLLM supports streaming through its OpenAI-compatible endpoint.
API Compatibility: vLLM provides an OpenAI-compatible API server out of the box. TGI has its own REST API format (though it is straightforward).
Multi-Model Serving: TGI supports loading multiple LoRA adapters simultaneously. vLLM also added multi-LoRA support more recently.
Ecosystem: TGI integrates natively with Hugging Face Hub, Inference Endpoints, and Optimum. vLLM integrates well with LangChain, LlamaIndex, and Ray Serve.
Speculative Decoding: Both engines now support speculative decoding, which can significantly reduce latency for certain models.

Getting Started with vLLM

Let's set up a vLLM server and send requests to it. The fastest way to start is using the official Docker image or installing via pip.

Installing vLLM

# Install vLLM via pip (requires CUDA-capable machine)
pip install vllm

# Or use the official Docker image
docker run --gpus all -p 8000:8000 \
    --rm --ipc=host \
    vllm/vllm-openai:latest \
    --model meta-llama/Llama-2-7b-chat-hf

Launching the OpenAI-Compatible Server

# Start the server with a model of your choice
python -m vllm.entrypoints.openai.api_server \
    --model mistralai/Mistral-7B-Instruct-v0.2 \
    --tensor-parallel-size 1 \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.9 \
    --port 8000

The --tensor-parallel-size flag controls how many GPUs to use for tensor parallelism. The --gpu-memory-utilization flag determines what fraction of GPU memory vLLM is allowed to use for the KV cache and model weights.

Sending Requests to vLLM

import openai

client = openai.OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="dummy-key"  # vLLM does not require a real key by default
)

response = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function to reverse a linked list."}
    ],
    max_tokens=512,
    temperature=0.7,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Using vLLM as a Python Library

If you do not need an HTTP server and want to embed inference directly in your application, vLLM provides a Python API:

from vllm import LLM, SamplingParams

# Load the model
llm = LLM(
    model="meta-llama/Llama-2-7b-chat-hf",
    tensor_parallel_size=1,
    max_model_len=4096,
    gpu_memory_utilization=0.85
)

# Define sampling parameters
sampling_params = SamplingParams(
    temperature=0.8,
    top_p=0.95,
    max_tokens=256
)

# Batch inference — vLLM handles batching automatically
prompts = [
    "Explain quantum computing in one paragraph.",
    "Write a haiku about the ocean.",
    "What are the benefits of containerization?"
]

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt}")
    print(f"Generated: {generated_text}\n")

Getting Started with TGI

TGI is most commonly deployed via Docker, which bundles all dependencies and optimized kernels. Let's walk through a complete setup.

Launching a TGI Container

# Pull and run the TGI Docker image
docker run --gpus all -p 8080:80 \
    -v $HOME/data:/data \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model mistralai/Mistral-7B-Instruct-v0.2 \
    --max-total-tokens 8192 \
    --max-batch-size 32 \
    --quantize bitsandbytes

Key flags to know: --max-total-tokens sets the maximum sequence length per request, --max-batch-size controls how many requests can be processed simultaneously, and --quantize enables weight quantization to reduce memory usage.

Querying the TGI Server

import requests

url = "http://localhost:8080/generate"

payload = {
    "inputs": "Write a Python function to reverse a linked list.",
    "parameters": {
        "max_new_tokens": 512,
        "temperature": 0.7,
        "top_p": 0.95,
        "do_sample": True,
        "return_full_text": False
    }
}

response = requests.post(url, json=pptload)
result = response.json()
print(result["generated_text"])

Streaming Responses from TGI

TGI supports Server-Sent Events for real-time token streaming, which is ideal for chat interfaces:

import requests

url = "http://localhost:8080/generate_stream"

payload = {
    "inputs": "Explain how transformers work step by step.",
    "parameters": {
        "max_new_tokens": 1024,
        "temperature": 0.7,
        "do_sample": True,
        "return_full_text": False
    }
}

with requests.post(url, json=payload, stream=True) as response:
    for line in response.iter_lines():
        if line:
            # TGI streams JSON objects prefixed with "data:"
            decoded = line.decode("utf-8")
            if decoded.startswith("data:"):
                import json
                chunk = json.loads(decoded[5:])
                print(chunk["token"]["text"], end="", flush=True)

Using TGI with the Hugging Face Client

from huggingface_hub import InferenceClient

client = InferenceClient(model="http://localhost:8080")

# Non-streaming generation
result = client.text_generation(
    prompt="Summarize the benefits of microservices architecture.",
    max_new_tokens=300,
    temperature=0.5
)
print(result)

# Streaming generation
for token in client.text_generation(
    prompt="Write a short story about a robot learning to paint.",
    max_new_tokens=500,
    stream=True
):
    print(token, end="", flush=True)

Performance Benchmarking

To make an informed decision, you should benchmark both engines on your specific hardware with your specific models and prompt distributions. Here is a simple benchmarking script using vLLM:

import time
from vllm import LLM, SamplingParams

llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.2", gpu_memory_utilization=0.9)
sampling_params = SamplingParams(temperature=0.0, max_tokens=128)

# Generate a batch of prompts
prompts = ["What is the capital of France?"] * 100

start = time.time()
outputs = llm.generate(prompts, sampling_params)
elapsed = time.time() - start

total_tokens = sum(len(o.outputs[0].token_ids) for o in outputs)
throughput = total_tokens / elapsed

print(f"Total time: {elapsed:.2f}s")
print(f"Total tokens generated: {total_tokens}")
print(f"Throughput: {throughput:.1f} tokens/second")

Run a similar benchmark against TGI by sending concurrent requests with a tool like locust or asyncio with aiohttp. Compare not just throughput but also tail latency (p95, p99), which matters for user-facing applications.

Best Practices

Choose Based on Your Workload

If your primary metric is raw throughput for many concurrent requests — for example, a batch processing pipeline or a high-traffic API — vLLM's PagedAttention typically delivers better memory utilization and higher request concurrency. If you need rich streaming, diverse quantization options, and tight Hugging Face ecosystem integration, TGI is the stronger choice.

Right-Size Your GPU Memory

Both engines let you control how much GPU memory is allocated. Setting these values too high can cause out-of-memory errors under load; setting them too low wastes capacity. Monitor your actual KV cache usage and adjust --gpu-memory-utilization (vLLM) or --max-batch-size and --max-total-tokens (TGI) accordingly.

Use Quantization When Memory-Constrained

If you cannot fit a model in GPU memory at full precision, use quantization. TGI supports bitsandbytes (easiest, NF4/INT8), GPT-Q, and AWQ. vLLM supports AWQ, GPTQ, and FP8 on newer GPUs. Always benchmark quality degradation after quantization — some models are more sensitive than others.

Enable Continuous Batching

Both engines support continuous batching by default, which dynamically inserts new requests into the batch as older requests complete. Never disable this in production — it is the single most impactful optimization for serving variable-length requests.

Monitor and Profile

Both vLLM and TGI expose Prometheus metrics. Track request latency, queue depth, GPU utilization, and token throughput. Set up alerts for queue buildup, which indicates that your instance is saturated and you need to scale horizontally or upgrade your GPU.

# vLLM exposes metrics at /metrics (Prometheus format)
curl http://localhost:8000/metrics

# TGI exposes metrics at /metrics as well
curl http://localhost:8080/metrics

Consider Speculative Decoding

For latency-sensitive applications, speculative decoding can reduce time-to-first-token and overall generation time by using a small draft model to propose tokens that the larger model verifies. Both vLLM and TGI support this, though configuration differs. Test whether your model pair benefits — gains are model-dependent.

Secure Your Endpoints

Both servers expose HTTP endpoints with no authentication by default. In production, place them behind an API gateway or reverse proxy that handles authentication, rate limiting, and TLS termination. vLLM supports API key validation via the --api-key flag; TGI does not have built-in auth, so external protection is mandatory.

Conclusion

vLLM and TGI are both excellent inference engines, and the right choice depends on your specific requirements. vLLM shines when raw throughput and memory efficiency are the top priorities, thanks to its PagedAttention architecture and excellent OpenAI API compatibility. TGI excels in scenarios that demand rich streaming, broad quantization support, and seamless integration with the Hugging Face ecosystem. For many teams, the best approach is to benchmark both on representative workloads using the actual hardware and models you plan to deploy — the performance gap can vary significantly based on model size, sequence length distribution, and concurrency patterns. Whichever you choose, following the best practices around memory tuning, quantization, monitoring, and security will ensure your LLM serving infrastructure is both performant and production-ready.