vLLM Local Deployment Guide: High-Throughput LLM Serving 2026

vLLM is the fastest open-source LLM serving framework for batched inference. Where Ollama handles one request at a time, vLLM uses PagedAttention to serve dozens of concurrent users at near-maximum GPU throughput. This guide sets it up for local use.

vLLM vs Ollama: When to Use Which

Scenario	Ollama	vLLM
Single user, casual chat	Best choice	Overkill
Multi-user API (5+ concurrent)	Queues requests	Handles batch natively
Maximum throughput batch jobs	Serial only	3-5x more efficient
GGUF models	Full support	HF weights only

Installation

pip install vllm

# Or with CUDA 12.1 (recommended)
pip install vllm --extra-index-url https://download.pytorch.org/whl/cu121

Starting a vLLM Server

# Serve Qwen3.6-7B with OpenAI-compatible API
python -m vllm.entrypoints.openai.api_server   --model Qwen/Qwen2.5-7B-Instruct   --gpu-memory-utilization 0.90   --max-model-len 32768   --host 0.0.0.0   --port 8000

# Access via OpenAI SDK
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
response = client.chat.completions.create(
    model="Qwen/Qwen2.5-7B-Instruct",
    messages=[{"role": "user", "content": "Hello"}]
)

Batch Inference for Maximum Throughput

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")
params = SamplingParams(temperature=0.7, max_tokens=512)

prompts = [
    "Explain transformer architecture",
    "What is GGUF format?",
    "Best GPU for 70B models?",
]

# vLLM processes all prompts in parallel with PagedAttention
outputs = llm.generate(prompts, params)
for output in outputs:
    print(output.outputs[0].text[:200])

Hardware Requirements for vLLM

vLLM requires HuggingFace format models in bf16/fp16 — it does not support GGUF. This means larger VRAM requirements than llama.cpp. For vLLM-compatible GPU setups, check LLMHardware.io's vLLM hardware guide.

• 7B model: 16GB VRAM minimum (bf16)
• 14B model: 28GB VRAM (use 2x RTX 3090 or A100)
• 32B model: 64GB VRAM (4x RTX 4090 or 2x A100 80GB)