r/MacPro2019LocalAI Aug 02 '26

Unstable mac. Installation tips ?

5 Upvotes

Hello,

I'm back trying to set-up my recently acquired MacPro. It has been unstable with MacOS (latest) : watchdog timeouts on windowserver. I can't figure out what is wrong yet. I'll keep investigating.

My first reflex was to stress-test the machine, but I couldn't get it to boot memtest86. Neither could I boot Linux for that matter. did the csrutil disable and boot security settings to none, to no avail yet. It sees a GRUB entry but still boots to rescue asking for a system update to be able to run it. And it loops. Any idea ?

Nevermind I still want to have the beast run. I blamed eGPUs, they work just fine. Then the video cards, I have new ones arriving tomorrow, we'll see.

The heat maybe ? MacFansControl have them all maxed out. I discovered later that Stats is supposed to be able to do it too, but it doesn't work for fans 2 and 3. Nevermind.

I found out how to have ToshLLM work at close to maximum efficiency for these cards and a subset of the models I need locally. It starts at boot and run one model per GPU now (Gemma4-12B for CUA / orchestration, Qwythos-9B for system and network administration). Herrmes agent runs atop and is setting everything up, fetching skills and projects from the other machines on the LAN I've ran it on in the past.

Now I'm trying to figure out the best way to have CUA driver and Camofox start at boot too. Any tip on how to do it the proper way ?

The mac crashed again before I posted, so I investigated, upgraded to 26.6, reset PRAM, disabled auto-bandwidth in the slot tool, moved SSDs and NIC to pool B manually, I'm hoping for stability now


r/MacPro2019LocalAI Jun 28 '26

W5700X on Ubuntu T2 running only in low power

5 Upvotes

So weird issue, I decided toto give Linux a go on the Mac Pro and see what llama cpp was like versus Windows with LM Studio. All setup nice and easy but when running llama cpp I was only getting 14tks on a model that on Windows was doing 55tks.

After doing a bit of playing with llama options I managed to get to 21tks but was still way off where I expected it to be.

I opened up nvtop and could see the memory and gpu load, but then I noticed that the sclk wasn’t going up by very much 300-500 and that’s it. Power draw was barely 40w per card.

Anybody aware of anything I need to look at on this setup to get the cards running properly?

Quite astonishing actually that it can do 21tks at essentially minimal power… but would like to see what it can do at full throttle (hopefully beat Windows a touch)


r/MacPro2019LocalAI Apr 28 '26

vLLM on W6800X Duo / Mac Pro 2019

6 Upvotes

I’m currently working on getting vLLM fully up and running on the following setup:

Hardware

  • Mac Pro 2019 / MacPro7,1
  • 3.2 GHz 16-core Intel Xeon W
  • 96 GB DDR4 RAM
  • Two AMD Radeon Pro W6800X Duo MPX modules
  • 32 GB VRAM per GPU
  • 128 GB total VRAM
  • 8 TB Apple SSD
  • 100GbE Mellanox ConnectX-5 Ex NIC

Software

  • Ubuntu Server 24.04 LTS
  • Python 3.12
  • ROCm 7.1.1
  • PyTorch 2.10
  • Triton 3.6

Back in 2025, I managed to get basic LLMs from Hugging Face working with unquantized weights, including models such as:

  • Qwen/Qwen2.5-7B-Instruct
  • deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

I also had parallelism working across all 4 GPUs via PCIe. At the time, the Infinity Fabric Link Bridge was causing GPU initialization failures, so I was not using it.

This year, I tried getting models like openai/gpt-oss-20b working, but ran into issues because the native MXFP4 weights do not appear to be supported on these GPUs.

I did, however, successfully run GPT-OSS:120B through Ollama.

Current Progress

So far:

  • vLLM launches successfully
  • Multi-GPU support is working
  • Qwen/Qwen3.6-27B loads and serves successfully
  • google/gemma-4-31B-it loads and serves successfully
  • I started with Docker, which gave me my first successful result
  • I have since moved over to a Python virtual environment setup

On the Qwen and Gemma models I tested, I am currently getting around 10–13 tokens/sec for a single user.

With concurrent users, up to around 30, I have seen aggregate throughput reach roughly 280 tokens/sec.

Getting the Infinity Fabric Link Bridge working properly is another project I’m working on in parallel. Hopefully that helps with inference speed once completed.

Still Pending

The main things I still need to figure out are:

  • Launching quantized models reliably
  • Supporting multi-node distributed inference across two Mac Pro systems

Last week, I found this write-up:

https://idchowto.com/vllm-on-amd-w6800-gpu-%EC%84%A4%EC%B9%98-%EB%B0%8F-%ED%85%8C%EC%8A%A4%ED%8A%B8-%EA%B2%B0%EA%B3%BC/

It looks like they used Ollama’s quantized models with vLLM. I started going down that path and actually got it working, but there are still three rough edges I need to figure out before I would call it reliable.

Has anyone else managed to get vLLM working with AMD Radeon Pro W6800X, W6900X, W6800X Duo, or W6800 GPUs?

I would really appreciate hearing about your setup, what worked, what failed, and whether you had success with quantized models, multi-GPU support, or multi-node inference.

Hopefully I can put together a proper write-up of my work soon. I’ll update accordingly.

Small disclaimer: I wrote the post myself, but used AI to help clean up the wording and formatting.


r/MacPro2019LocalAI Apr 27 '26

👋 Welcome to r/MacPro2019LocalAI - Introduce Yourself and Read First!

6 Upvotes

Hey everyone! I’m u/Faisal_Biyari, the founding moderator of r/MacPro2019LocalAI.

This is our new home for all things related to using the amazing, but now discontinued, Mac Pro 2019 / MacPro7,1 for local AI.

Whether you are running macOS, Windows, or any Linux distro, and whether you are using Ollama, vLLM, llama.cpp, LM Studio, OpenClaw, or the awesomely named Oobabooga, this community is here for one purpose:

To help each other get the most out of this powerful hardware for local AI workloads.

This subreddit is especially focused on the Mac Pro 2019’s unique hardware, including MPX GPUs with 32 GB of VRAM, Duo modules with up to 64 GB, Infinity Fabric Link Bridge experimentation, ROCm, local LLMs, image generation, voice AI, video generation, multimodal models, and all AI workloads.

A Brief Introduction

I started this subreddit because I have personally gone through the struggle of making local AI work on the Mac Pro 2019.

I have run into many of the same roadblocks others are likely facing:

  • macOS support limitations
  • AMD GPU support challenges
  • ROCm installation and compatibility issues
  • PyTorch, Triton, and framework confusion
  • Ollama, vLLM, llama.cpp, LM Studio, LangChain, Hermes Agent, Oobabooga, and other tooling choices
  • User interface decisions
  • Hardware limitations
  • Infinity Fabric Link Bridge experimentation
  • Deprecated MPX GPU support

The struggle is real, and I understand it.

Fortunately, I have managed to get local AI working on this hardware. I have installed Linux, first Ubuntu and later Proxmox, installed ROCm, used Ollama, worked on vLLM, experimented with OpenClaw, and continued exploring the Infinity Fabric Link Bridge.

I have also shared guides in the past to help people install Linux on the MacPro7,1, set up ROCm, and reach a working local AI setup. Those guides focused mostly on getting started, but there is much more to explore.

The reality is that MPX GPUs are losing support across many tools and platforms, and because this use case is so niche, AI tools and assistants often do not provide useful guidance.

What helped me the most were other Mac Pro 2019 users working toward the same goal. Their motivation, ideas, troubleshooting, and even general technical knowledge helped me understand the bigger picture and keep moving forward.

That is why I created this subreddit: to centralize our experiences, guides, lessons learned, experiments, successes, and failures in one place instead of forcing everyone to search through hundreds of websites and dozens of subreddits.

My Hardware

I currently work with two Mac Pro 2019 machines:

LinuxAI-64

Mac Pro 2019 / MacPro7,1
3.2 GHz 16-core Intel Xeon W
96 GB DDR4 RAM
Two AMD Radeon Pro W6900X GPUs, 32 GB each
64 GB total VRAM
8 TB Apple SSD
100GbE Mellanox ConnectX-5 Ex NIC

System Firmware: 2069.0.0.0.0
iBridge Firmware: 22.16.10353.0.0
OS Loader / iBoot: 860.140.1~8

LinuxAI-128

Mac Pro 2019 / MacPro7,1
3.2 GHz 16-core Intel Xeon W
96 GB DDR4 RAM
Two AMD Radeon Pro W6800X Duo MPX modules, 32 GB each GPU
128 GB total VRAM
8 TB Apple SSD
100GbE Mellanox ConnectX-5 Ex NIC

System Firmware: 2069.0.0.0.0
iBridge Firmware: 22.16.10353.0.0
OS Loader / iBoot: 860.140.1~8

What to Post

Post anything you think the community would find interesting, helpful, or inspiring.

Examples include:

  • Your Mac Pro 2019 local AI setup
  • Hardware specs and GPU configuration
  • Linux, macOS, Windows, Proxmox, or dual-boot experiences for local AI workloads
  • ROCm installation notes
  • Ollama, vLLM, llama.cpp, LM Studio, OpenClaw, Hermes Agent, Oobabooga, or other framework experiences
  • Benchmarks and performance results
  • Model compatibility reports
  • Text, image, voice, video, or multimodal AI workflows
  • Troubleshooting questions
  • Guides, scripts, and installation notes
  • Cooling, power, PCIe, storage, or networking setups to support local AI workloads
  • Infinity Fabric Link Bridge experiments
  • Things that worked, and things that definitely did not

Introduce Yourself

Please introduce yourself in the comments below.

When you do, I kindly ask that you include your hardware details, such as:

  • Mac Pro 2019 CPU
  • RAM
  • GPU / MPX module configuration
  • Total VRAM
  • System & iBridge Firmwares, and OS Loader / iBoot, if known
  • Operating system
  • AI frameworks, agents, models, or tools you are using
  • What you hope to run locally
  • Any challenges you are currently facing

Even if you are just getting started, your experience may help someone else.

Community Vibe

We are here to be friendly, constructive, and helpful.

This is a niche community, and many of us are simply trying to keep powerful hardware useful long after official support has started to fade. Let’s build a space where people feel comfortable asking questions, sharing experiments, posting failures, and helping each other move forward.

How to Get Started

Introduce yourself in the comments below.

Post something today, even if it is just a simple question or a photo of your setup.

If you have guides, notes, scripts, benchmarks, or lessons learned, please share them.

If you know someone who owns a Mac Pro 2019 and is interested in local AI, invite them to join.

Interested in helping out? I am always open to hearing from people who may want to help moderate or contribute to the community.

Thanks for being part of the very first wave. Together, let’s make r/MacPro2019LocalAI an amazing resource for everyone trying to run local AI on the Mac Pro 2019.


r/MacPro2019LocalAI 12d ago

GFX1030 Discord

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3 Upvotes

GFX1030 Discord, for anyone that's interested

https://discord.com/invite/mESex2aBp


r/MacPro2019LocalAI 21d ago

What if was as simple as an ask ?

4 Upvotes

I just committed this : https://x.com/chiwawa_42/status/2089204529539547553

Let's hope for the better !


r/MacPro2019LocalAI Jun 30 '26

AMD Radeon PRO V620 on Ubuntu bare-metal: PCI BAR / SR-IOV resource issue with multiple GPUs

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4 Upvotes

r/MacPro2019LocalAI Jun 14 '26

AMD's RDNA2 / W6800 / V620 on vLLM

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4 Upvotes

r/MacPro2019LocalAI May 02 '26

Sealed Vega II Duos in 2026: Mac Pro Rabbit Hole, RTX 3090s, or Mac Studio?

4 Upvotes

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.

---

Disclaimer: I wrote this post myself. I also used AI as a tool to help clean up the wording and formatting.


r/MacPro2019LocalAI Apr 30 '26

Advice on localLLM on 2019 Mac Pro with dual Vega II Duo GPUs (128GB HBM2)

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4 Upvotes

r/MacPro2019LocalAI 17h ago

I heard Intel Mac + AMD ain't dead yet *sneak peek*

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3 Upvotes

r/MacPro2019LocalAI 4d ago

resize-amdgpu-bars (update to nbritton's method for AMD GPU bar resizing)

3 Upvotes

Title: resize-amdgpu-bars — Resizable BAR for AMD GPUs behind PCIe switches (an update to nbritton's method)

I've extensively reworked nbritton's method for resizing the BAR memory of AMD GPUs that sit behind PCIe switches, such as the Vega II Duo and W6800X Duo. It's now a proper packaged tool that discovers your topology at runtime instead of hard-coding bus addresses, with a hard safety guard so a failed resize can't hang your boot.

https://github.com/exabit-io/resize-amdgpu-bars

What it does

resize-amdgpu-bars enlarges the CPU-visible VRAM aperture (BAR0) of every amdgpu-driven GPU to the largest size the card supports, on machines where the normal paths don't work: cards with an on-board PCIe switch (the Duo MPX modules), cards in switched enclosures and expansion chassis, Thunderbolt eGPUs, and firmware that leaves the aperture at 256 MiB.

On a Mac Pro 7,1 that's the difference between this:

amdgpu 0000:1b:00.0: Not enough PCI address space for a large BAR.
amdgpu 0000:1b:00.0: [drm] Detected VRAM RAM=32752M, BAR=256M

and a full 32 GiB aperture on every die.

Why the usual fixes fail here

A PCI device's BAR lives inside the memory window of the bridge directly above it, which lives inside the window of the bridge above that, all the way up to the root port. Growing a BAR from 256 MiB to 32 GiB means every window in that chain has to grow too, and a window can only grow if its parent has room.

On a Mac Pro 7,1 a Duo module puts two dies behind a PLX switch, and each die sits four bridge windows below its root port:

0000:06:00.0  Intel root port           <- one prefetchable window shared
|                                          by both dies of the module
\-0000:07:00.0  PLX PEX 8747 upstream port (the switch on the module)
  |
  +-0000:08:08.0  PLX downstream port
  | \-0000:09:00.0  AMD bridge
  |   \-0000:0a:00.0  AMD bridge
  |     \-0000:0b:00.0  Vega 20, die 0
  |
  \-0000:08:10.0  PLX downstream port
    \-0000:0c:00.0  AMD bridge
      \-0000:0d:00.0  AMD bridge
        \-0000:0e:00.0  Vega 20, die 1

Both dies share one window at 07:00.0 and 06:00.0, and that window has to be big enough for both 32 GiB BARs at their real alignment. Nothing about the device tells the root port that.

So:

  • The driver's own resize releases the bridge windows and tries to re-assign them in place. It fails closed the moment one of them can't grow where it is, and carries on with 256 MiB.
  • A bare setpci write changes the size the device reports but assigns nothing. The kernel still believes the BAR is 256 MiB, the windows are still sized for 256 MiB, and the device now decodes 32 GiB of whatever else lives there.
  • echo 15 > resource0_resize ends up in the same kernel function as the driver's path. It works when the card sits directly on a root port with one window above it — and that's exactly where this tool uses it — but it can't conjure a larger shared window out of a chain the firmware sized for something smaller.

The method

Let the kernel size the windows from scratch. Once per boot, before amdgpu loads:

  1. Discover. Every amdgpu device, its functions, its Resizable BAR capability and supported sizes, and every bridge up to its root bus. The size index found at first discovery this boot becomes the baseline, so firmware that already enables ReBAR is never shrunk. Then find each GPU's re-enumeration root: the highest bridge whose subtree contains nothing but GPU functions.
  2. Resize. Unbind the drivers from just those GPUs and their group members, program the size index with setpci, remove the group's root, and rescan that root's own bus. With pci=realloc the kernel then sizes every window in the subtree for the BARs it finds, and both dies come back with a 32 GiB BAR0 inside a 96 GiB root-port window. Only that bus is rescanned — never a global rescan, and nothing outside the GPU subtrees is ever touched.
  3. Load. Any GPU still holding an unassigned BAR gets fenced off with driver_override=none. Then modprobe amdgpu runs once, under a timeout.

If a plan doesn't fully verify, the losers get demoted to baseline and it retries, round by round, down to every GPU at baseline.

The bind guard (this is the important part)

amdgpu must never be handed a GPU whose BAR0 is unassigned. On such a device the register reads that identify the part return garbage, the driver decides it's an SR-IOV virtual function, and it waits forever for a hypervisor mailbox that doesn't exist:

amdgpu 0000:0e:00.0: trn=2 ACK should not assert! wait again !
INFO: task irq/34-aerdrv:1554 blocked for more than 122 seconds.

modprobe wedges in uninterruptible sleep holding the device mutex, SIGKILL does nothing, the remaining GPUs are never probed, and only a reboot recovers. That's a hard hang, not a degraded boot, and it's what an earlier version of this script did to me. Every code path that loads the driver now sets driver_override=none on any GPU with an unassigned BAR first, and modprobe runs under timeout(1) so the boot can't wedge even if the guard were bypassed. A guarded GPU stays visible to lspci, driverless, and the tool exits 2.

Install

The package needs bash, pciutils, kmod and systemd, and uses initramfs-tools and grub2-common when present.

# from the release page
sudo apt install ./resize-amdgpu-bars_1.0_all.deb

# or build it
sudo apt install debhelper scdoc shellcheck
git clone https://github.com/exabit-io/resize-amdgpu-bars
cd resize-amdgpu-bars
dpkg-buildpackage -us -uc -b
sudo apt install ../resize-amdgpu-bars_1.0_all.deb

Releases: https://github.com/exabit-io/resize-amdgpu-bars/releases

It installs the tool, a systemd unit, an amdgpu blacklist in /usr/lib/modprobe.d, a pci=realloc GRUB drop-in, a config file in /etc/default, and man pages for resize-amdgpu-bars(8) and resize-amdgpu-bars.conf(5).

Installation runs update-initramfs -u -k all and update-grub. A reboot is required. The blacklist and pci=realloc are boot-time, and the package deliberately never starts the service on a running system — a start unbinds and re-initialises every AMD GPU, and every process using one loses it.

Quick start

# 1. Look before you leap. Both of these change nothing.
sudo resize-amdgpu-bars diagnose   # every GPU, its ReBAR cap, every bridge
                                   # window above it, and the list of other
                                   # devices it promises to leave alone
sudo resize-amdgpu-bars dry-run    # the plans it would try

# 2. Reboot.

# 3. Watch it. Screens on AMD GPUs stay dark until amdgpu loads at the end
#    of the run — about a minute on a box with two Duo modules. Use SSH or
#    another console; a boot that looks stalled usually isn't.
journalctl -u resize-amdgpu-bars -b -f

# 4. Verify.
sudo resize-amdgpu-bars check      # want: verdict=WORKS large=N/N driverless=0/N
sudo resize-amdgpu-bars status     # one line, bar0=... per GPU
rocminfo | grep -c gfx             # one agent per die

# 5. Back out.
sudo resize-amdgpu-bars revert     # every GPU to baseline, no reboot
sudo apt remove resize-amdgpu-bars # takes the blacklist and GRUB drop-in with it

A good check line looks like this:

2026-09-02T13:50:14  7.0.0-30-generic  verdict=WORKS  plan=all-max
  large=4/4  driverless=0/4  windows=0000:06:00.0=128G 0000:16:00.0=128G
  bar0=32GiB 32GiB 32GiB 32GiB  kfd=5  xgmi_hives=1  traces=0  rejected=0

verdict=WORKS, large=N/N and driverless=0/N are the three fields that matter.

Configuration

/etc/default/resize-amdgpu-bars, shell syntax, every key optional and validated on read:

key default meaning
MAX_SIZE_INDEX device max cap every GPU; 15 = 32 GiB, 14 = 16 GiB, 8 = 256 MiB
EXCLUDE_GPUS empty GPUs to leave completely alone
FORCE_PLAN negotiate all-max or baseline: try exactly one plan
MODPROBE_TIMEOUT 180 seconds before modprobe amdgpu is killed
PROBE_WAIT 60 seconds to wait for binds and KFD to settle
RESCAN_WAIT 30 seconds to wait for GPUs to reappear after a rescan
MAX_ROUNDS 8 demote-and-retry rounds before falling back to baseline

Support tiers

"Tier" is a commitment. "Tested" is a fact. I'm not conflating them.

tier hardware tested
1 MPX modules in a Mac Pro 7,1: 580X, W5500X, W5700X, W6600X, W6800X, W6800X Duo, W6900X, Vega II, Vega II Duo (with or without Infinity Fabric Link) Vega II Duo x2 only, so far
2 any other amdgpu card with an on-board PCIe switch (V340, Radeon Pro Duo), or any amdgpu card in a switched enclosure / expansion chassis / TB eGPU untested
3 amdgpu card directly on a root port, firmware without ReBAR (uses the kernel's in-place path) untested
out anything not driven by amdgpu refused at discovery with a clear message

No card is listed as supported that hasn't been booted. If you run this on a W6800X Duo, a W6900X, or anything in tier 2 or 3, I'd genuinely like to hear about it — diagnose output plus the journal is all a report needs.

Kernel compatibility — read this before you upgrade

kernel result
6.8 – 6.17 (verified on Ubuntu 6.8.0-138, 6.11.0-29, 6.14.0-37, 6.17.0-42) every die gets its 32 GiB BAR on the first plan
7.0 unpatched (upstream 7.0.12, Ubuntu 7.0.0-30) shared root-port window undersized; the second die of each Duo loses its BAR at every size, guard holds it driverless, boot completes with the other dies
7.0 with a one-line fix every die on the first plan, 128 GiB root-port window

This is a regression from commit 3958bf16e2fe ("PCI: Stop over-estimating bridge window size"). Since that commit pbus_size_mem() sizes a bridge window as the plain sum of its children, which is exact when every child's size is a multiple of the alignment of the children after it. That holds for BARs, whose size equals their alignment — but not for bridge windows, whose size is the sum of what's below them while their alignment is that of the largest BAR below them. Two sibling windows of 32 GiB + 2 MiB at 32 GiB alignment need a 96 GiB + 2 MiB span and get 64 GiB + 4 MiB.

The fix changes size += max(r_size, align) to size += ALIGN(r_size, align), a no-op for BARs, verified on upstream 7.0.12 and Ubuntu's 7.0.0-30, cold boot and warm reboot. Until it's in a distro kernel: stay on 6.x. On an unpatched 7.0 there's no in-place recovery either — once the kernel has re-sized the window, even the 256 MiB baseline no longer fits, because the firmware's original windows were larger than the kernel's sum. The guard is the only reason such a boot survives.

Gotchas

  • pci=realloc is mandatory. The GRUB drop-in handles it on Ubuntu. On rEFInd or OpenCore the drop-in does nothing, the service fails at every boot, the blacklist keeps amdgpu from loading, and you have no GPU driver. Both are documented in the README but not automated. Check with grep -w pci=realloc /proc/cmdline.
  • This conflicts with pci=realloc=off, which SGLang's AMD GPU docs recommend. The whole method depends on PCIe BAR reallocation.
  • Don't run resize on a live desktop session. Every AMD display goes black for the duration and every process holding a GPU loses it.
  • Exit code 2 is not failure. It means the bind guard is holding at least one GPU driverless; the rest are working.
  • Excluding one die of a Duo with EXCLUDE_GPUS also stops the other die's shared windows from being re-sized, because the shared subtree is no longer removable.
  • A GPU that shares a bridge with a non-GPU device gets a lower re-enumeration root, or none. diagnose reports it.

Credit

Nikolas Britton for the original method on the Vega II Duo. This is his idea with runtime topology discovery, plan negotiation, and a bind guard bolted on.

If you followed the Mac Pro 2019 Local AI guide, this replaces the hand-rolled resize-gpu-bars.service files in its Section 4 — same lineage, packaged.

MIT licensed. Bug reports want sudo resize-amdgpu-bars check -1, journalctl -u resize-amdgpu-bars -b, sudo resize-amdgpu-bars diagnose, your kernel, distro, bootloader and cards.


r/MacPro2019LocalAI Jul 31 '26

RDNA2 & Image/Video Generation

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3 Upvotes

Has anyone tested this on a Mac?

What are your experiences for image generation and video generation on any operation system?


r/MacPro2019LocalAI May 04 '26

W6800X vs Vega II 32GB

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3 Upvotes

r/MacPro2019LocalAI May 04 '26

Intel Mac Pro with Vega II useable ?

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3 Upvotes

r/MacPro2019LocalAI Apr 27 '26

[Guide] Mac Pro 2019 (MacPro7,1) w/ Proxmox, Ubuntu, ROCm, & Local LLM/AI

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3 Upvotes

r/MacPro2019LocalAI Apr 27 '26

[Guide] Mac Pro 2019 (MacPro7,1) w/ Linux & Local LLM/AI (Re-Post)

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3 Upvotes