Okay, and for this computer and for this GPU, I have another GPU. And just to remind you, the name of this computer is a HP Z440 with Windows 10. My plan is to change that to a Ubuntu and then also change the graphics card from the NVIDIA Quadro K620. And I'm wondering if how many PCI slots, it looks like there are extra ones on this computer, but I can't really tell. And would my strategy be to add an additional, because I have another GPU, a NVIDIA Tesla T4 16 gigabyte. And what I had planned to do was place that on either replace it with the Quadro or to add it in addition to the Quadro. And so just a few, like it would be good to get some advice on, should I install Linux on this computer first and then start tuning it or should I install the other GPU? GPU. And my preference would be to get onto Linux because Windows, I think, doesn't help me. Like, I want to build a Linux server, probably Ubuntu LTS server, on this computer and then do inference with the GPU with small models with the Tesla T4 16 gigabyte. And so just getting the instructions on how do you think the best way is to do that would be really good.

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Accepted Answer

This is a great project! Let me give you a comprehensive guide for turning your HP Z440 into a Linux-based inference server with the Tesla T4.

**About the HP Z440 and PCI Slots**

The HP Z440 is a solid workstation for this purpose. It typically has three PCI Express slots: one PCIe x16 slot which is your primary graphics slot where the Quadro K620 currently sits, one PCIe x8 slot which is electrically x8 but physically x16, and one PCIe x4 slot. The Tesla T4 uses a PCIe x16 connector but works fine in x8 mode with minimal performance loss for inference workloads.

**My Recommended Strategy**

I'd suggest this order of operations:

First, install Ubuntu Server LTS before swapping GPUs. Here's why: The Quadro K620 has straightforward driver support and gives you video output during installation. Ubuntu Server 22.04 LTS or 24.04 LTS are both excellent choices. You can do a clean install, get your base system configured, install NVIDIA drivers and CUDA toolkit, and verify everything works before adding complexity.

Second, regarding the GPU decision, I'd recommend replacing the K620 rather than running both. The Tesla T4 is a much more capable card for inference, it draws only 70 watts so power isn't an issue, and running both cards adds unnecessary complexity and power draw. The K620 is really just a display adapter at this point and won't help with inference.

However, there's one important consideration: The Tesla T4 is a headless compute card with no video outputs. So you'll need to either manage the server remotely via SSH which is the recommended approach for a server, or keep the K620 installed just for display output if you need local access, or use a cheap basic display card if you want local video.

**Step by Step Installation Plan**

Step one, with the K620 still installed, create a bootable Ubuntu Server USB drive, install Ubuntu Server LTS, and during installation enable OpenSSH server so you can manage it remotely.

Step two, after Ubuntu is running, update the system by running sudo apt update and sudo apt upgrade. Then install NVIDIA drivers using sudo apt install nvidia-driver-535 or whatever the current recommended version is. Also install the CUDA toolkit with sudo apt install nvidia-cuda-toolkit. Reboot and verify with nvidia-smi.

Step three, power down completely and unplug the power cable. Ground yourself to avoid static discharge. Remove the K620 and install the Tesla T4 in the primary x16 slot. The T4 is a single-slot, low-profile card so it should fit easily.

Step four, boot up and SSH into the server. Run nvidia-smi to verify the T4 is detected. You should see the Tesla T4 with 16 gigabytes of memory.

Step five, for inference workloads install your preferred framework. For most small model inference, you'd want PyTorch with CUDA support, or you might look at vLLM, Ollama, or text-generation-inference depending on what models you plan to run.

**Important Notes**

On power supply, the Z440 typically has a 700 watt power supply which is plenty for the T4's 70 watt draw.

On cooling, the T4 is passively cooled and designed for server airflow. The Z440 has decent airflow but monitor temperatures initially. If it runs hot, you might need to improve case ventilation.

On BIOS settings, you may need to enter BIOS and ensure PCIe slots are set to Gen3 mode for best performance.

The T4 is excellent for inference with small to medium models. You should be able to run 7 billion parameter models quantized, or smaller models at full precision, quite comfortably with 16 gigabytes of VRAM.

Would you like more specific guidance on any part of this process, like the Ubuntu installation steps, setting up a particular inference framework, or the physical GPU installation?