I know these are both older GPUs at this point and that the W6800X generally outperforms the Vega II, but I'm wondering if there are any use cases where a Vega II is better (maybe due to the higher memory bandwidth?)
I ended up with both of these GPUs and am planning to keep one alongside my RX 6900 XT.
>"For some, Linux fails to boot, for some it's okish, for some it's good"
u/AdityaGarg8 said that to me back in November 2024, when he was kind enough to help guide me through installing Ubuntu on my Mac Pro 2019.
After a lot of testing, I think that quote perfectly describes the current state of Linux on the Mac Pro 2019, especially when using Apple’s MPX AMD GPUs with the Infinity Fabric Link jumper or bridge installed.
From my testing, the main issue appears to involve the Infinity Fabric Link jumper/bridge.
On newer kernels, especially kernel 6.8 and later, some GPUs with the Infinity Fabric Link installed do not initialize correctly. In my case, this has shown up as amdgpu initialization failures and psp -22 errors.
On kernel 5.15.0, the GPUs initialize more successfully, but I still see errors, especially SDMA-related errors. So I would describe 5.15.0 as partial support, not full support.
So far, my practical summary is:
psp -22.This is not meant to be a final technical diagnosis. It is a report of what I and others are seeing on real Mac Pro 2019 hardware.
For local AI, the most important factors are usually:
On multi-GPU setups, VRAM is not automatically pooled into one shared memory space. Each GPU has its own VRAM, and when a workload is split across multiple GPUs, the GPUs need to communicate with each other.
Without a direct GPU-to-GPU interconnect, the normal path is usually something like:
GPU0 -> CPU / PCIe -> GPU1
That means traffic has to go through the PCIe path, with the CPU/platform sitting in the middle.
The AMD MPX GPUs in the Mac Pro 2019 are based on PCIe 4.0-capable GPUs, but the Mac Pro 2019 platform itself provides PCIe 3.0 bandwidth. A PCIe 3.0 x16 link has a theoretical maximum of about 15.75 GB/s per direction.
This is where Infinity Fabric Link becomes interesting.
With proper support, Infinity Fabric Link should allow direct GPU-to-GPU communication:
GPU0 -> GPU1
That removes the normal CPU/PCIe middle step for supported GPU-to-GPU traffic.
Apple rates the Infinity Fabric Link connection at up to 84 GB/s in each direction. That is more than five times the theoretical one-direction bandwidth of PCIe 3.0 x16.
In theory, that could be a major advantage for multi-GPU workloads, especially workloads where GPUs need to exchange data frequently.
For local AI, this could matter most in cases like:
My current answer is:
Not reliably, at least not on the W6800X Duo and W6900X in my testing.
Some users have reported better results with Vega II / Vega II Duo, and it is possible that older MPX GPUs behave differently. But with the W6800X Duo and W6900X, I do not currently see clean, reliable Infinity Fabric Link behavior under Linux.
To be clear, I am not saying Linux has no AMD GPU support. The GPUs themselves can work under Linux. The issue appears to be specifically around the Infinity Fabric Link Jumper/Bridge with the MPX GPU implementation; firmware/PSP initialization and how the AMDGPU driver handles this hardware combination.
Personally, I am experimenting with:
The goal is to see how far this partial support can go, whether the link actually becomes active, and whether there is any measurable bandwidth advantage when it does.
I am also watching newer stacks such as:
Hopefully, proper support or a workaround appears for these newer kernels.
There is already activity on the DRM AMD GitLab here:
https://gitlab.freedesktop.org/drm/amd/-/work_items/3793
If you have a Mac Pro 2019 with MPX GPUs, especially Vega II, Vega II Duo, W6800X, W6800X Duo, or W6900X, please consider sharing your results there.
Useful information would include:
dmesg / journalctl errorsShare your experience.
What hardware do you have?
What OS and kernel are you using?
Does the system boot?
Do all GPUs initialize?
Does removing the Infinity Fabric Link jumper or bridge change anything?
Have you found a kernel version where it works better?
Hopefully, with more of us testing, reporting, and giving this issue attention, we can help establish better Linux support for these powerful MPX GPUs on the Mac Pro 2019.
Disclaimer: I wrote this post myself, but used AI to help clean up the wording and formatting.
Resources:
Hi, I made a post recently looking for advice running local LLMs on my 2019 Mac Pro. At the time, I was split on what GPUs to use, but I happened upon a really good deal for a pair of Vega II Duo GPUs (4x Vega II 32GB GPUs for 128GB total HBM2 with 1Tb/s bandwidth each). I currently have the small infinity fabric (IF) jumpers on them (connects the two GPUs inside each module) but don't have the IF bridge to connect the two modules together, though from what I'm reading I might want to skip the IF link stuff all together as support for it is spotty.
I'm looking for advice on the following:
Hi !
I have an opportunity to get a 2019 Mac Pro with a 32GB Vega2 board. I know LM Studio isn't supported on MacOS for Intel, so what other way to turn this machine into a local AI server would you recommend ?
Windows isn't an option, Linux could be possible, though I'd like to keep it running Mac OS if possible. Main objective is to run MCP servers alongside on this dedicated machine.
What kind of speed could I get from the Vega II chip ? (similar to Mi50 32GB from what I could find)
Thanks !
I was recently approached by a couple of local flippers.
The first offered me three sealed AMD Radeon Pro Vega II modules for $915 USD each.
The second offered me four sealed AMD Radeon Pro Vega II Duo modules for $1,200 USD each.
Neither offer includes the Infinity Fabric Link Bridge, of course. I also came to learn that, unlike the W6800X Duo and W6900X, the Vega II did not ship with the Infinity Fabric Link Bridge in the box. Only the Infinity Fabric Link Jumper shipped with the Duo model.
Still, I have to admit, I am impressed that sealed Vega II and Vega II Duo modules are still showing up on the market in 2026.
At first, the prices felt like a catch. But then I had to stop and seriously think about whether it was actually worth the hassle to invest in them for my use case.
I took a ride with my AI of choice and discussed it. The conclusion was pretty practical:
For a local AI workload, especially if the goal is raw usefulness, software support, and flexibility, it suggested going with four NVIDIA RTX 3090s instead of four Vega II Duos.
And honestly, that makes sense, even though the NVIDIA RTX 3090 is almost as old as the Vega II GPUs.
But at the same time, I am having so much fun with this whole “local AI on a discontinued Mac Pro” journey that part of me is still tempted.
There is something really interesting about pushing the Mac Pro 2019 / MacPro7,1 as far as it can go, even if the more logical path is to move to standard PC GPUs with better AI support.
So now I am genuinely curious:
What would you do?
Would you buy the sealed Vega II / Vega II Duo modules and keep exploring the Mac Pro 2019 rabbit hole?
Would you skip the Apple MPX route entirely and put the money toward a multi-GPU RTX 3090 setup instead?
Or would you shoot for a Mac Studio for the same budget?
The logical answer seems obvious, but the fun answer is not always the logical one.
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Disclaimer: I wrote this post myself. I also used AI as a tool to help clean up the wording and formatting.
I found some conflicting information regarding the Infinity Fabric Link hardware for the AMD Radeon Pro Vega II Duo. I feel fairly confident in the conclusions below, but I would love some further input from the community.
The Jumpers are straightforward. The Duo MPX GPU models are the only ones that use them, since their purpose is to link the two GPUs inside a single MPX module.
The Bridges are where the conflicting information appears.
From what I have found:
Apple’s documentation clearly states that the Vega II, W6800X, and W6900X support an Infinity Fabric Link Bridge. For the W6800X Duo, Apple’s documentation states support for both Jumper and Bridge.
However, Apple’s documentation only mentions the Vega II Duo using the Infinity Fabric Link Jumper, not a Bridge. Apple even shows two Vega II Duo modules installed in one 2019 Mac Pro, but only shows the Jumpers, with no Bridge, despite it being a dual-Vega II Duo setup.
This also lines up with the box contents. The W6800X and W6900X ship with their corresponding Bridges, and the W6800X Duo ships with both its Jumper and Bridge. The Vega II Duo only ships with the Jumper. I have confirmed those box contents myself. I have also read that the Vega II ships with Bridge A2326, but I have not personally confirmed that.
My best guess is that it comes from a mix of assumptions, the fact that the A2326 Bridge physically fits the Vega II Duo, and third-party listings; especially MacSales / OWC stating that Bridge A2326 supports the Vega II Duo.
MacSales’ A2326 page is one of the first results that appears when searching for the part number. I was one of the users who saw this, believed it was true, and shared that information with others. However, based on Apple’s own documentation, the observed box contents, and other online data points, I now believe that the compatibility claim was a mistake.
First of all, it would physically fit. But after that, macOS would simply fail to boot. My best guess is that it would be similar to the problem Linux users are currently facing with Infinity Fabric Link, including psp -20 errors or BAR size issues.
To summarize support, my current understanding is:
| Part | Type | Supported GPU |
|---|---|---|
| A2326 | Bridge | Vega II only |
| A2329 | Jumper | Vega II Duo only |
| A2666 | Bridge | W6800X / W6900X |
| A2667 | Bridge | W6800X Duo only |
| A2668 | Jumper | W6800X Duo only |
Back to Linux and using the Infinity Fabric Links, this raises the question:
Vega II users with the Bridge would succeed, while Vega II Duo users with the Bridge would experience errors. On the other hand, Vega II Duo users with the Jumper would succeed as well.
If you are a Linux user with a Vega II or Vega II Duo, and you have tested either the Jumper or Bridge, please share your experience.
One remaining [For Fun] question I have, for both macOS and Linux users:
Would the A2666 Bridge work with one W6800X and one W6900X?
This is interesting, because A2666 is the only Bridge associated with two different MPX GPU models. However, Apple’s documentation seems to describe it only in same-model configurations: two W6800X modules or two W6900X modules.
So my assumption is that a mixed W6800X + W6900X setup is probably unsupported in macOS, but I would be interested to hear from anyone who has physically tested it.
I would also be interested to hear from anyone who has physically tested the A2326 Bridge with two Vega II Duo MPX modules, since that appears to be the main point of conflicting information.
Disclaimer: I wrote this post myself. I also used AI as a tool to help clean up the wording and formatting.
References:
Many of us have used some form of AI by now, and most of us have seen the power and convenience it can provide.
Back in 2015, I wrote a simple Visual Basic program in Visual Studio. It took me almost a full week to complete, including online searches, trial and error, and asking questions on Stack Overflow.
In 2025, with the help of ChatGPT, I completed a piece of iOS software in just a few hours, despite having never coded for iOS before.
In the right hands, AI is a very powerful tool.
I want access to that tool regardless of the circumstances I am in. I do not want my access to depend entirely on subscriptions, cloud availability, internet access, changing policies, rate limits, or someone else deciding what I can and cannot run.
That is where local AI comes in.
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I was fortunate enough to receive a Mac Pro 2019 back in 2020.
In 2023, while searching for SSDs for it, I stumbled across a seller offering brand new, sealed AMD Radeon Pro W6800X Duo and W6900X MPX modules for about 75% of today’s used market price.
I bought one mainly for the Thunderbolt ports. I also bought three more with other goals in mind.
In 2024, with the release of Llama 3, and later with ROCm becoming a more serious path for my hardware, I committed to using these GPUs for local AI. The problem was that my Mac Pro was still my main desktop, and I could not repurpose it entirely for AI work.
So I ended up investing about $3,000 USD into two additional machines, including shipping and tax:
I then installed the MPX GPUs into each machine.
The first machine is **LinuxAI-64**, with dual AMD Radeon Pro W6900X GPUs.
The second machine is **LinuxAI-128**, with dual AMD Radeon Pro W6800X Duo GPUs.
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Because ROCm mainly supports Ubuntu and RHEL-based distributions, I chose Ubuntu Server 22.04 LTS.
I chose Ubuntu because I had prior experience with it. I chose the Server variant to minimize non-AI GPU load. I chose 22.04 LTS because it was the latest ROCm-supported Ubuntu version at the time.
Then came the frameworks. I was mainly considering:
I initially wanted to go with vLLM, but I ended up using Ollama because vLLM does not support my hardware.
Then came the next layers:
And then reality hit:
That is a lot of work for a local AI setup that may still be only half as capable as cloud AI today.
I still went for it though.
---
I love the idea of multi-agent workloads.
In practical terms, I want AI agents that can help act as:
And honestly, I want to see how far I can push the idea of building something like a 20–30 person company using local AI agents as the supporting workforce, running on my own hardware, and using only the investment I have already made.
If it works, I get what I wanted.
If it does not, I gain the experience.
Either way, I learn.
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I need a community around me.
Community helps me work better, stay motivated, and increase my productivity.
Online, that means communities like:
There are many others too, both on Reddit and elsewhere.
Locally, I am based in Riyadh, the capital of Saudi Arabia. I have a very small local circle interested in local AI, but I would love to find more people nearby who are in the same boat: same hardware, same goals, same interest in pushing local AI forward.
Someone like that would help motivate me, challenge me, and push me to improve.
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For the purpose of growing this community, and documenting the work I have put into these machines, I plan to release a series of guides over the next few weeks.
The goal is to document success after success as I work toward my final local AI setup.
The general idea is simple:
The more that is documented, the easier it becomes for others to follow the same path, join the discussion, brainstorm solutions, and hopefully even pass me and innovate beyond what I have done.
That would benefit the community as a whole, and it would also help me on my own journey.
I am interested to know what you think is important to cover.
I am also interested in any solution, framework, stack, or workflow that you think is worth testing.
Consider this a brainstorming session.
I have already written some starter guides, and I plan to update and fine-tune them first. After that, I want to go deeper into frameworks, especially vLLM. I also plan to work on OpenClaw and document my experience with it.
I am a big believer in copy-paste instructions. I format and rebuild systems from scratch often, and having clean, repeatable instructions makes the recovery period much faster.
Let me know what you think, what I should look into, and what topics would be most useful as I work toward the final goal.
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^(Disclaimer: I wrote this post myself. I also used AI as a tool to help clean up the wording and formatting.)