How to run a small AI training and inference pipeline on APAC low-latency GPU rental (Hong Kong & Singapore)

If you’re building for users across Asia-Pacific, keeping inference close to your audience is the fastest way to cut response times and speed up iteration. This hands-on guide shows how overseas/APAC developers can complete a small fine-tune plus inference deployment within a 24–168 hour window using APAC low-latency GPU rental in Hong Kong or Singapore. We’ll prioritize instant deploy, privacy-friendly onboarding (no formal identity checks; crypto-friendly payments when supported by your provider), and pragmatic model choices that fit a single 24GB GPU.

Why Hong Kong & Singapore for APAC latency

Hong Kong and Singapore are recognized interconnection hubs in the region, with dense data center ecosystems, cloud on-ramps, and subsea cable landings that enable low-latency routing across Asia-Pacific. Equinix describes these metros as central to APAC colocation and interconnectivity in their APAC colocation overview. Console Connect has also written about expanded peering opportunities and 100G ports in APAC exchanges, such as HKIX and DE-CIX Singapore, in their 2024 peering update.

How do you confirm the best region for your users today? Run a quick latency check before committing your Day or Week plan:

• From your client location, ping a test server or your cloud instance in both regions; pick the one with lower RTT.
• Use iPerf3 to measure throughput and jitter: on the instance, iperf3 -s; from your client, iperf3 -c <server_ip> or UDP mode iperf3 -c <server_ip> -u -b 1M -t 10. See the iPerf3 docs.

Which GPU to pick for short runs

Both options below offer 24GB of VRAM. Your choice should follow workload fit and budget:

• RTX 40 class (e.g., RTX 4090 24GB): Strong single-GPU performance for fine-tuning small/medium models and high-throughput inference. Good default for mixed training + inference in one sprint.
• Tesla P40 24GB: Older enterprise GPU that’s often cost-effective for inference and lightweight fine-tunes. If your workload is primarily inference or small adapters, this can be a pragmatic pick.

Think of it this way: if your 24–168 hour plan includes a few hours of training plus serving, lean RTX 40; if it’s inference-first with modest tuning, P40 can stretch the budget.

Quickstart on APAC low-latency GPU rental: Day/Week micro-pipeline in five steps

Below is a provider-neutral workflow using Docker and the NVIDIA Container Toolkit. Estimated time to complete: 1–3 hours of setup, then 1–6 hours of fine-tune depending on data size.

1. Launch a GPU instance in Hong Kong or Singapore

• Choose the region nearest to end users (verify with ping/iPerf). Ensure a public IP and open port for your future API.

2. Install/verify NVIDIA Container Toolkit on the host

• Follow NVIDIA’s install guide, then configure the Docker runtime: sudo nvidia-ctk runtime configure && sudo systemctl restart docker.
• Verify GPU visibility in containers: docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi.

3. Prepare a working container environment

• Start a base container with common ML tooling:

docker run -it --rm --gpus all \
  -p 8000:8000 \
  -v $HOME/work:/work \
  nvidia/cuda:12.4.1-base-ubuntu22.04 bash

# Inside the container
apt-get update && apt-get install -y python3-pip git
pip3 install --upgrade pip
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu124
pip3 install transformers accelerate peft bitsandbytes vllm fastapi uvicorn

4. Run a minimal fine-tune (LoRA/QLoRA) on a 7B–9B model

• Use 4-bit loading to fit within 24GB, and keep batch sizes small:

# train_lora.py (minimal sketch)
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model
from transformers import BitsAndBytesConfig

model_name = "Qwen/Qwen2.5-7B-Instruct"  # example: check the model card
bnb_cfg = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_cfg,
    device_map="auto",
)

lora_cfg = LoraConfig(r=8, lora_alpha=16, lora_dropout=0.05, target_modules=["q_proj","v_proj"])  # adjust targets per model
model = get_peft_model(model, lora_cfg)

# TODO: tokenizer, dataset loader, training loop with gradient accumulation, frequent checkpoints to /work/checkpoints

Note: bitsandbytes runtime quantization typically isn’t saved with save_pretrained(); reload with the same config or consider AWQ/GPTQ for permanent quantized artifacts. See HF docs.

5. Serve inference with vLLM (OpenAI-compatible)

# still inside the container
vllm serve Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 8000

• Test with a simple curl:

curl -X POST http://<your_instance_ip>:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-7B-Instruct",
    "messages": [{"role": "user", "content": "Hello from APAC!"}],
    "max_tokens": 64
  }'

If latency from your client feels jarring, try the other region and compare.

Practical example: end-to-end on a Day plan in Hong Kong or Singapore

Disclosure: SurferCloud is our product. The platform supports instant deployment, Hong Kong (RTX 40) and Singapore (Tesla P40) GPU availability, hourly/daily/weekly billing, and unlimited bandwidth. For account setup and console steps (SSH keys, security groups), see this step-by-step deployment guide.

• Launch: Start an RTX 40 Day plan in Hong Kong when you expect a mix of training + inference; pick Tesla P40 in Singapore for inference-first runs.
• Verify GPU: SSH in and run nvidia-smi; then run the Docker nvidia/cuda test.
• Fine-tune: Use the minimal LoRA script above with a small dataset; checkpoint adapters to a mounted volume or external object storage.
• Serve: Expose vLLM on port 8000; confirm OpenAI-compatible responses via curl.
• Latency check: Run ping and iperf3 from your client to ensure response times meet your target.
• Clean up: Stop the container, persist checkpoints, snapshot the instance if needed, and shut down when idle to control costs.

Model notes: ChatGPT API, GLM-4.5 variants, Qwen

• ChatGPT API: If your use case integrates OpenAI’s ChatGPT API, you can keep training separate and only deploy a lightweight server as a proxy or task orchestrator; no GPU needed for the API itself.
• GLM-4.5: Consider GLM-4.5-4B or GLM-4.5-9B variants for single-GPU experiments; check the official model cards (GLM-4.5-4B, GLM-4.5-9B) and use bitsandbytes 4-bit or AWQ/GPTQ where appropriate.
• Qwen: Qwen2.5-7B-Instruct is a practical starting point on 24GB; see the official model card. Always confirm memory needs and supported quantization in each card before training or serving.

Troubleshooting essentials

• GPU not visible in containers: Ensure NVIDIA Container Toolkit is installed and Docker runtime configured; test with docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi. See NVIDIA’s troubleshooting guide.
• CUDA/driver mismatch: Prefer recent CUDA base images that match the host driver; see the NVIDIA install guide.
• Out-of-memory during fine-tune/inference: Reduce batch size and sequence length; apply 4-bit loading; use LoRA adapters; monitor VRAM via nvidia-smi. See PEFT LoRA methods and Transformers docs.
• High latency from client: Switch regions (HK vs SG), enable batching in vLLM/SGLang, and test network conditions with iPerf3.

Cost/time hygiene and cleanup checklist

Day plan (24h) fits quick experiments, small LoRA fine-tunes, and short-lived inference validation. Week plan (168h) suits extended testing and more robust endpoint hardening. Practical hygiene:

• Rightsize: Pick the GPU based on training vs inference balance.
• Avoid idle time: Shut down instances when not actively training or serving.
• Checkpoint frequently: Save adapters and model state to a persistent volume or external storage.
• Snapshot if needed: Create a snapshot before shutdown to resume later.
• Release resources: Stop containers, release any reserved IPs, and clean storage.

Where to go next

• Ready to try a Day or Week plan and request a trial? Visit SurferCloud’s contact page.

Plan	GPU Model	VRAM	Compute Power	GPU	CPU & RAM	Bandwidth	Disk	Duration	Location	Price	Deploy
RTX40 GPU Day	RTX40	24GB	83 TFLOPS	1	16C 32G	2Mbps	200G SSD	24 Hours	Hong Kong	$4.99 / day	Order Now
Tesla P40 Day	Tesla P40	24GB	12 TFLOPS	1	4C 8G	2Mbps	100G SSD	24 Hours	Singapore	$5.99 / day	Order Now
RTX40 GPU Week	RTX40	24GB	83 TFLOPS	1	16C 32G	2Mbps	200G SSD	168 Hours	Hong Kong	$49.99 / week	Order Now
Tesla P40 Week	Tesla P40	24GB	12 TFLOPS	1	4C 8G	2Mbps	100G SSD	168 Hours	Singapore	$59.99 / week	Order Now