GPU Dedicated Server in Hong Kong: AI Model Training and Inference (2026)

Running large AI models — fine-tuning LLMs, training image models, or serving 70B parameter inference — requires GPU hardware that a standard VPS cannot provide. A GPU dedicated server in Hong Kong gives Asia-Pacific AI teams access to A100 or H100 GPUs on CN2 GIA-connected hardware, enabling training and inference workloads that serve mainland Chinese users at 30–55ms — far better latency than AWS or GCP GPU instances in US regions.

GPU Server vs Cloud GPU: The Asia-Pacific Case

Step 1: Initial GPU Server Setup

<code">apt update && apt upgrade -y

# Install CUDA toolkit (for NVIDIA GPUs)
# Check GPU model first
nvidia-smi

# Install CUDA 12.x
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
dpkg -i cuda-keyring_1.1-1_all.deb
apt update
apt install -y cuda-toolkit-12-4 nvidia-cudnn

# Add CUDA to PATH
echo 'export PATH=/usr/local/cuda/bin:$PATH' >> /etc/profile.d/cuda.sh
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> /etc/profile.d/cuda.sh
source /etc/profile.d/cuda.sh

# Verify
nvidia-smi
nvcc --version

Step 2: Install PyTorch and ML Stack

<code">apt install -y python3.11 python3.11-venv python3-pip

python3.11 -m venv /opt/ai-env
source /opt/ai-env/bin/activate

# Install PyTorch with CUDA 12.4
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

# Verify GPU is available
python3 -c "import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))"

# Common ML packages
pip install transformers accelerate bitsandbytes datasets \
  peft trl sentencepiece protobuf einops flash-attn \
  fastapi uvicorn httpx

Step 3: LLM Inference Server with vLLM

vLLM is the most efficient open-source LLM inference engine — using PagedAttention to maximise GPU memory utilisation and serve multiple concurrent requests efficiently.

<code">pip install vllm

# Serve Qwen2.5-7B-Instruct (excellent Chinese + English)
python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen2.5-7B-Instruct \
    --host 127.0.0.1 \
    --port 8000 \
    --dtype auto \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.90 \
    --tensor-parallel-size 1 \   # Set to 2 or 4 for multi-GPU
    --served-model-name qwen2.5-7b

# For larger models on multi-GPU:
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4 \   # Distribute across 4 GPUs
    --gpu-memory-utilization 0.95

Systemd Service for vLLM

<code">cat > /etc/systemd/system/vllm.service << 'EOF'
[Unit]
Description=vLLM Inference Server
After=network.target

[Service]
Type=simple
User=ai
WorkingDirectory=/opt/ai-env
Environment="PATH=/opt/ai-env/bin:/usr/local/cuda/bin:/usr/bin:/bin"
Environment="CUDA_VISIBLE_DEVICES=0,1,2,3"
ExecStart=/opt/ai-env/bin/python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen2.5-7B-Instruct \
    --host 127.0.0.1 \
    --port 8000 \
    --gpu-memory-utilization 0.90
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

systemctl enable --now vllm

Step 4: Expose Inference API via Nginx

<code">cat > /etc/nginx/sites-available/ai-api << 'EOF'
upstream vllm {
    server 127.0.0.1:8000;
    keepalive 16;
}

server {
    listen 443 ssl http2;
    server_name ai.yourdomain.com;

    ssl_certificate /etc/letsencrypt/live/ai.yourdomain.com/fullchain.pem;
    ssl_certificate_key /etc/letsencrypt/live/ai.yourdomain.com/privkey.pem;

    # API key authentication at Nginx level
    if ($http_x_api_key != "YOUR_STRONG_API_KEY") {
        return 401 '{"error": "Unauthorized"}';
    }

    client_max_body_size 10m;
    proxy_read_timeout 120s;   # LLM responses can take time

    location / {
        proxy_pass http://vllm;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_buffering off;   # Stream tokens as they generate
    }
}
EOF

ln -s /etc/nginx/sites-available/ai-api /etc/nginx/sites-enabled/
certbot --nginx -d ai.yourdomain.com
nginx -t && systemctl reload nginx

Test the API (OpenAI-Compatible)

<code"># vLLM exposes OpenAI-compatible API
curl https://ai.yourdomain.com/v1/chat/completions \
  -H "X-Api-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-7b",
    "messages": [{"role": "user", "content": "用中文解释什么是CN2 GIA"}],
    "stream": true,
    "max_tokens": 500
  }'

Step 5: Fine-Tuning with LoRA

<code"># Fine-tune a model on your custom dataset using QLoRA
# (Quantised LoRA — trains large models on smaller GPUs)

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import get_peft_model, LoraConfig, TaskType
from trl import SFTTrainer
import torch

model_name = "Qwen/Qwen2.5-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    load_in_4bit=True   # QLoRA: quantise to 4-bit to fit in GPU memory
)

# LoRA configuration — only fine-tunes a small adapter
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=16,              # Rank — higher = more capacity, more memory
    lora_alpha=32,
    lora_dropout=0.1,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"]
)

model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# trainable params: 20,971,520 || all params: 7,615,676,416 || 0.28%

training_args = TrainingArguments(
    output_dir="/opt/models/qwen-finetuned",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    fp16=True,
    logging_steps=10,
    save_steps=100,
)

# Load your training dataset
from datasets import load_dataset
dataset = load_dataset("json", data_files="/opt/training/data.jsonl")

trainer = SFTTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
    dataset_text_field="text",
    max_seq_length=2048,
)

trainer.train()
trainer.save_model("/opt/models/qwen-finetuned")

Step 6: GPU Monitoring

<code"># Real-time GPU monitoring
watch -n 1 nvidia-smi

# More detailed monitoring
nvidia-smi dmon -s pucvmet -d 5
# Shows: power, utilisation, clock, memory, temperature, fan

# GPU utilisation over time
pip install gpustat
gpustat --watch

# Prometheus GPU metrics (for Grafana dashboard)
# Install DCGM Exporter:
docker run -d \
  --gpus all \
  --name dcgm-exporter \
  --restart unless-stopped \
  -p 127.0.0.1:9400:9400 \
  nvcr.io/nvidia/k8s/dcgm-exporter:latest

# Grafana Dashboard: import ID 12239 (NVIDIA DCGM Exporter)

GPU Memory Requirements by Model

Conclusion

A GPU dedicated server in Hong Kong closes the gap between AI capability and Asia-Pacific performance — mainland Chinese users access your AI-powered application at 30–55ms rather than the 200–350ms of US-hosted GPU cloud instances. With vLLM serving OpenAI-compatible endpoints and fine-tuning via LoRA adapters on your custom dataset, the GPU server functions as both your inference infrastructure and training platform. The fixed monthly cost is typically 30–60% lower than equivalent cloud GPU on-demand pricing for continuous workloads.

Access GPU hardware in Hong Kong: Browse Server.HK dedicated server configurations — contact sales for GPU server availability and pricing for your AI workload requirements.