NVIDIA T4 & Turing Architecture: Optimizing AI Inference Workloads in 2019

Let’s be honest: running inference on a Tesla V100 is like driving a Formula 1 car to the grocery store. It’s expensive, inefficient, and frankly, bad architecture. Since NVIDIA dropped the T4 based on the Turing architecture late last year, the landscape of AI hosting has shifted. If you aren't looking at INT8 precision for your production models yet, you are leaving about 40% of your performance on the table.

I recently audited a deployment for a computer vision startup here in Oslo. They were burning through capital running ResNet-50 on P100 instances, complaining about cost. We migrated them to T4s, enabled mixed-precision, and saw their throughput double while cutting costs by half. Here is the technical breakdown of how we did it, and how you can replicate this stack on a CoolVDS GPU instance.

The Hardware: Why T4 is the Inference King

The T4 is a single-slot, 70W card. Compare that to the 250W+ TDP of the older Pascal cards. But the real magic isn't the power draw; it's the Tensor Cores. With Turing, we finally get multi-precision computing that actually works out of the box with CUDA 10.

For Norwegian businesses dealing with data privacy (GDPR), running these workloads locally is non-negotiable. You cannot just ship sensitive customer data to a US-based API. You need raw compute sitting in an Oslo datacenter, governed by Norwegian law (and powered by our cheap hydro energy).

Setting Up the Environment: Ubuntu 18.04 + CUDA 10.0

The T4 requires at least NVIDIA driver version 410.x. On a fresh CoolVDS instance running Ubuntu 18.04 LTS, don't rely on the default repositories. They are often outdated.

1. Driver Installation

First, purge any old nouveau drivers that might interfere with the install.

# Clean up existing drivers
sudo apt-get purge nvidia*

# Add the graphics PPA for the latest stable drivers
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

# Install the headless driver (we don't need X11 on a server)
sudo apt-get install nvidia-driver-418

# Reboot to load the kernel module
sudo reboot

Once you are back up, verify the card is recognized. If you don't see the T4 listed here, do not proceed.

nvidia-smi

You should see something like this:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.43       Driver Version: 418.43       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   43C    P0    26W /  70W |      0MiB / 15079MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

The Container Strategy: nvidia-docker2

It is March 2019, and unfortunately, standard Docker still does not natively support GPUs without a wrapper. We rely on nvidia-docker2. If you try to run a heavy TensorFlow workload on bare metal, you will enter dependency hell. Isolate it.

# Install the repository
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update

# Install the wrapper
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

Pro Tip: Always set the default-runtime to nvidia in your /etc/docker/daemon.json if you are running a dedicated AI node. This saves you from typing --runtime=nvidia every single time.

Optimizing TensorFlow 1.13 for Turing

Here is where the "Performance Obsessive" mindset pays off. By default, TensorFlow 1.13.1 uses Float32. The T4 excels at Float16 (Mixed Precision). You need to explicitly tell your graph to use mixed precision to unlock the Tensor Cores.

Here is a Python snippet for your model loader:

import tensorflow as tf

# Check if GPU is visible
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

# Enable Mixed Precision (Experimental in TF 1.13)
config = tf.ConfigProto()
config.graph_options.rewrite_options.auto_mixed_precision = 1

# Prevent TF from eating 100% of VRAM immediately
config.gpu_options.allow_growth = True

# Start session with Turing optimizations
sess = tf.Session(config=config)

Without the auto_mixed_precision rewrite, the T4 behaves like a slightly faster P4. With it enabled, we see inference times on image classification tasks drop from 45ms to roughly 12ms per image in batches.

Latency, NIX, and the Norwegian Advantage

Why does geography matter for AI? Throughput is metric A, but latency is metric B. If your inference server is in Frankfurt but your users are in Trondheim, you are adding 20-30ms of round-trip time (RTT) purely on network physics.

By hosting on CoolVDS in Oslo, you are peering directly at NIX (Norwegian Internet Exchange). Your RTT to local users drops to sub-5ms. For real-time applications—like video analytics or automated financial trading—that difference is massive.

Security & Data Sovereignty

We all watched the GDPR implementation last year. The Norwegian Datatilsynet is strict. If you are processing medical data or personal IDs through your neural network, utilizing a provider that guarantees data residency within Norway simplifies your compliance audit significantly. CoolVDS infrastructure is built with this specific legal framework in mind.

Benchmarks: T4 vs CPU

Just to drive the point home, we ran a quick ResNet-50 inference test (Batch Size 128).

• Dual Xeon E5-2680 v4 (CPU only): 65 images/sec
• CoolVDS Instance + NVIDIA T4 (FP32): 850 images/sec
• CoolVDS Instance + NVIDIA T4 (Mixed Precision): 1400+ images/sec

The math is simple. You would need a rack full of CPUs to match a single T4 instance.

Final Thoughts

The T4 is the most significant hardware release for production AI we have seen in years. It allows us to move models from research (V100s) to production (T4s) without bankrupting the company. However, hardware is only half the battle. You need a properly configured OS, the right CUDA drivers, and a network that doesn't bottleneck your API responses.

If you are ready to stop simulating performance and actually deliver it, deploy a GPU-optimized instance today.

Need to test your model on Turing architecture? Spin up a CoolVDS T4 instance in Oslo. It takes less than 60 seconds.