High-Throughput LLM Serving with vLLM: Crushing Latency in the Nordics

Let’s be honest: standard HuggingFace Transformers inference pipelines are a disaster for production. If you are serving Llama 2 or Mistral 7B using the default AutoModelForCausalLM pipeline, you are effectively setting money on fire. The latency is acceptable for a hobby project, but for a real-time application? It’s unusable.

The bottleneck isn't just raw compute; it's memory management. Specifically, the Key-Value (KV) cache fragmentation. This is where vLLM and its PagedAttention algorithm changed the game earlier this year. We are talking about a 10x to 24x increase in throughput compared to naive implementations.

As a Systems Architect focused on the Norwegian market, I see another bottleneck that often gets ignored: Data Sovereignty and Network Latency. Sending prompts to OpenAI in the US is a GDPR nightmare (Schrems II says hello) and introduces 150ms+ of trans-Atlantic lag. Hosting your own inference engine in Oslo isn't just about compliance; it's about speed.

Here is how to architect a high-throughput LLM serving layer using vLLM, backed by the raw I/O power of CoolVDS.

The Architecture of Speed: PagedAttention

In traditional attention mechanisms, memory for the KV cache is allocated in contiguous chunks. But LLM generation is dynamic; you don't know how long the output will be. This leads to massive internal fragmentation—GPU memory is reserved but never used.

vLLM borrows a concept from operating systems: virtual memory paging. It breaks the KV cache into blocks that don't need to be contiguous in physical memory. This allows the GPU to batch requests far more aggressively.

Pro Tip: vLLM allows you to serve concurrent requests with a single model copy. On a typical A100 or high-end consumer GPU, you can batch dozens of users simultaneously without an OOM (Out Of Memory) crash.

Prerequisites & Setup

We are targeting a deployment on Ubuntu 22.04 LTS. You need a machine with CUDA 11.8 or 12.1 drivers installed. Python 3.10 is the sweet spot right now.

First, ensure your environment is clean. We don't want system-wide package conflicts.

# Create a virtual environment
sudo apt update && sudo apt install -y python3.10-venv
python3 -m venv vllm-env
source vllm-env/bin/activate

# Install vLLM (Current stable as of Nov 2023)
pip install vllm

# Install ray for distributed inference if you have multiple GPUs
pip install ray

Deploying an OpenAI-Compatible Server

The beauty of vLLM is that it can act as a drop-in replacement for the OpenAI API. You don't need to rewrite your client code. You just change the base_url.

Here is how to launch a server for the Mistral-7B-Instruct-v0.1 model. This model punches way above its weight class and is perfect for low-latency Norwegian chatbots.

python3 -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mistral-7B-Instruct-v0.1 \
  --host 0.0.0.0 \
  --port 8000 \
  --gpu-memory-utilization 0.95 \
  --max-num-batched-tokens 4096

Understanding the Flags

Productionizing with Systemd

Do not run this in a screen session. If the server reboots, your service dies. Create a proper systemd unit file. This ensures your inference engine comes up automatically and logs correctly.

[Unit]
Description=vLLM Inference Server
After=network.target

[Service]
User=root
Group=root
WorkingDirectory=/root/inference
Environment="HUGGING_FACE_HUB_TOKEN=your_token_here"
ExecStart=/root/inference/vllm-env/bin/python3 -m vllm.entrypoints.openai.api_server \
    --model mistralai/Mistral-7B-Instruct-v0.1 \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

Save this to /etc/systemd/system/vllm.service, then reload and start:

sudo systemctl daemon-reload
sudo systemctl enable vllm
sudo systemctl start vllm

The CoolVDS Factor: I/O and Latency

You might be wondering: "If the model runs in VRAM, why does the VPS disk speed matter?"

Two reasons: Model Loading and Swapping.

Modern LLMs are huge. A quantized 70B model can be 40GB+. When your service restarts (and it will), you need to read those safetensors from disk into VRAM instantly. On a standard HDD or cheap SSD VPS, this takes minutes. On CoolVDS NVMe instances, we saturate the PCIe bus, loading models in seconds. Downtime is minimized.

Furthermore, if you are building a RAG (Retrieval-Augmented Generation) pipeline, your Vector Database (like Weaviate or Milvus) is constantly hitting the disk to fetch context. If your disk I/O waits, your GPU waits. The GPU is the most expensive part of your stack—don't let a slow disk idle it.

Testing the Endpoint

Once your service is live, verify the throughput. If you are in Oslo and your server is on CoolVDS infrastructure, your network latency should be negligible (<5ms).

import requests
import time

url = "http://localhost:8000/v1/chat/completions"
headers = {"Content-Type": "application/json"}
data = {
    "model": "mistralai/Mistral-7B-Instruct-v0.1",
    "messages": [{"role": "user", "content": "Explain quantum entanglement in one sentence."}],
    "stream": False
}

start = time.perf_counter()
response = requests.post(url, headers=headers, json=data)
end = time.perf_counter()

print(f"Latency: {(end - start) * 1000:.2f} ms")
print(response.json()['choices'][0]['message']['content'])

Data Privacy in the Nordics

For Norwegian businesses, the Datatilsynet is watching. Using US-based APIs for sensitive customer data is becoming legally hazardous. By hosting vLLM on a CoolVDS instance within Norway, you ensure data never leaves the jurisdiction. You own the logs, you own the weights, and you own the compliance.

High-performance AI isn't just about the GPU. It's about the entire ecosystem: low-latency networking, NVMe storage, and rock-solid Linux optimization. Don't let your infrastructure be the bottleneck.

Ready to build your sovereign AI stack? Deploy a CoolVDS High-Performance instance today and keep your tokens local.