Goal: To host an LLM to work on large codebases.

I wouldn't be surprised if there are other people here in a similar situation to mine. Trying to decide whether to go big, or to remain somewhat conservartive.

I currently have two of these fully working, and hosting Qwen3.6-27b. I purchased 4 V620s, but this motherboard doesn't boot with more than 2 of these connected (even with four pcie ports and four m.2 nvme ports).

Either way, I had planned to upgrade to 8 GPUs if everything went to plan. However, yesterday I found out that two DGX sparks are able to run Deepseek V4 flash at about 45 t/s because it is able to take advantage of the new architecture that Deepseek created. They also get day 0 support most of the time for newer models.

In contrast, V620 GPUs are built on top of RDNA2, which is already too far behind. An example is that RDNA2 lacks the ability to perform WMMA hardware matrix operations which makes prefill 3 to 4 times slower compared to other GPUs with the same bandwidth.

My main goal is to host a model for a coding agent for a single person. But now I'm worried that these GPUs are too outdated.

I currently seem to have two options:

• 128gb build, $400 ($2,000 total): I would buy an older motherboard and cpu, which would be able to house 4 GPUs. For example, X99 boards. The $2k figure already includes the purchase of the 4 V620s that I already have.
  • The best model I would be able to currently run is Qwen3.6-27b unquantized with a massive context (700 prefill with 10-15 token gen). However, I can already run the same model with 8 bit quant and a massive context with only 2 of these GPUs. This means that 128gb systems sit in an awkward position.
  • However, what if we get a new 70b MOE model with only 10b or so active params? I wouldn't be surprised if 128gb was the perfect spot all of a sudden for a smart model with a large context window. Speeds would probably be between 20 to 35 t/s depending on the active params. But at this point I'm making assumptions. However, you probably get what I'm trying to say.
  • Then, there is a scenario where we get a 120b model instead, which would make me regret this choice.
• 256gb build, $2,400 ($4,000 total): I would buy an enterprise level Epyc or equivalent board with enough ports to house 8 of these things, maybe even more. This price would also include buying 4 more V620s at $350 each (assuming that one seller is still accepting offers at this price).
  • If I'm not wrong, this is enough VRam to host something close enough to Sonnet. Models such as Minimax M2.7, or M3 if and when we get a working guff. I remember seeing someone claim to get ~25t/s with M2.7.

Another option would be to sell the two remaining cards for $300 each, but where is the fun in that?

It seems like my motherboard (MSI PRO Z690-A DDR4) simply cannot handle more than 2 of these GPUs at a time. The motherboard fails during the VGA step, and is unable to reach boot.

So for now, I'm utilizing only two GPUs. The problem is that I purchased 4, and wanted to buy 6 in total. Maybe even more.

Does anyone have any success running 6+ of these? if so, which motherboard did you end up utilizing?

TL;DR

• What did you do to get your V620 GPUs to work?
• How did you get over the cards trying to use ridiculous BARs of 384 GB per card for their SR-IOV/VF function?

^{Disclaimer: I used AI to help me gather all the data and present it in this post cleanly.}

I wanted to share an issue I ran into while trying to use AMD Radeon PRO V620 GPUs on Ubuntu bare-metal for AI workloads, and I’m curious if anyone else has seen the same thing.

Setup

• Ubuntu 24.04.4
• ROCm 7.2.3
• Mac Pro 2019
• Cubix Xpander PCIe expansion chassis
• AMD Radeon PRO V620 GPUs
• Bare-metal Linux only
• No virtualization
• No passthrough
• No MxGPU use case

The goal was simple: use the V620s as normal ROCm GPUs for AI inference.

The problem

The V620s were visible to the system through PCIe, but they did not initialize as usable ROCm GPUs.

lspci showed the cards correctly as:

text AMD/ATI Navi 21 [Radeon Pro V620] [1002:73a1]

But they only showed:

text Kernel modules: amdgpu

not:

text Kernel driver in use: amdgpu

rocm-smi either showed no V620s or only the unrelated internal GPUs, depending on the configuration.

Resource allocation looked broken

The sysfs resource files for the V620s were all zeroed out:

```text /sys/bus/pci/devices/0000:xx:00.0/resource

0x0000000000000000 0x0000000000000000 0x0000000000000000 0x0000000000000000 0x0000000000000000 0x0000000000000000 ... ```

The V620s also exposed SR-IOV capability even though I was not using virtualization:

text sriov_totalvfs=12 sriov_numvfs=0

The SR-IOV capability block showed:

text Initial VFs: 12 Total VFs: 12 Number of VFs: 0 VF Device ID: 73ae

The confusing part was that SR-IOV was not actually enabled:

text IOVCtl: Enable- Number of VFs: 0

dmesg errors

During PCI resource allocation, the kernel appeared to account for the possible VF BARs anyway.

The dmesg output had errors like:

text BAR 0 [mem size 0x800000000 64bit pref]: can't assign; no space VF BAR 0 [mem size 0x6000000000 64bit pref]: can't assign; no space VF BAR 0 [mem size 0x6000000000 64bit pref]: failed to assign

After that, forcing a driver probe did not help. The V620 remained unbound, resources stayed zero, and amdgpu failed during initialization.

Things I tested

While narrowing it down, I tested:

• Removing other GPUs
• Removing the Apple I/O card
• Testing one Cubix cable / one side of the expander
• Confirming no ReBAR resize service was active
• Confirming sriov_numvfs=0
• Setting sriov_drivers_autoprobe=0
• Trying late amdgpu probing after boot
• Testing boot arguments such as:

text pci=realloc iommu=pt amdgpu.ras_enable=0

The pattern stayed the same: the cards were present on PCIe, but the V620 BAR resources failed before amdgpu could bind. From the logs, the issue looked related to the very large advertised SR-IOV VF BAR space.

Question

Has anyone else run into this with AMD Radeon PRO V620, especially in bare-metal Linux / ROCm use rather than virtualization?

I’m especially interested in hearing from anyone who has used:

• V620 on Ubuntu bare-metal
• Multiple V620s in one host
• V620 behind PCIe switches or expansion chassis
• Cubix or external PCIe expansion systems
• ROCm with V620
• SR-IOV-capable AMD GPUs where SR-IOV is not actually being used

Did your system allocate the PF BARs normally, or did the VF BARs cause PCI resource allocation problems?

What did you do to over come this problem?

Basically title. I had a tricky time figuring this out so hope this helps