I have been bothering the VM team for years for VM GPU pass through. I worked on the Apple Silicon Mac Pro and it would have made way more sense if you could run a linux VM and pass through the GPU that goes inside the case!
Sadly, as you can tell, they have not taken me up on my requests. Awesome that other people got it working!
It feels like half the problem in this blog post is dealing with memory access issues induced by QEMU and the VM boundary... it's probably something dumb I'm missing, but if you boot up Ubuntu in Docker, wouldn't the NVIDIA drivers still load? And then you wouldn't have to fight Apple about the memory management because OSX would still own the memory?
> but if you boot up Ubuntu in Docker, wouldn't the NVIDIA drivers still load?
Even if the drivers loaded, they can't talk to the GPU from within docker (unless one implements PCI passthrough). MacOS owns the PCI bus in this scenario.
> I still believe the lack of NVIDIA GPU support in the Mac Pro will go down as one of the greatest missed opportunities in tech.
I don’t know about that. Apple supported some full size GPUs in past product lines and the number of users was very small. Granted, LLMs change that demand but the audience for Mac Pro buyers who would use a full-size GPU that is impossible to obtain is almost nothing compared to their laptop sales.
Part of the reason the new Mac Pro failed to find an audience can definitely be blamed on macOS' hostility to third party hardware. Who knows what Apple would be worth if they beat Nvidia's Grace CPU to the datacenter market. It was certainly their opportunity.
The game benchmarks are fun but the LLM improvements are where this gets really interesting for practical use. I love Apple platforms as an approachable way to run local models with a lot of RAM, but their relatively slow prompt processing speed is often overlooked.
> Here you can see the big issue with Macs: the prompt processing (aka “prefill”) speed. It just gets worse and worse, the longer the prompt gets. At a 4K-token prompt, which doesn’t seem very long, it takes 17 seconds for the M4 MacBook Air to parse before we even start generating a response. Meanwhile, if you strap the eGPU to it, it’ll only take 150ms. It’s 120x faster.
The prefill problem goes unnoticed when you’re playing around with the LLM with small chats. When you start trying to use it for bigger work pieces the compute limit becomes a bottleneck.
The time to first token (TTFT) charts don’t look bad until you notice that they had to be shown on a logarithmic scale because the Mac platforms were so much slower than full GPU compute.
> As much as I hate to admit it, step one in most of my projects now is to ask AI about it. Maybe it’ll tell me something I don’t know.
Or, more likely, it will tell you something it doesn't know.
Reminds me of yesterday, when I was arguing with ChatGPT that the 5070TI was an actual video card. It kept trying to correct me by saying I must have meant a 4070ti, since no such 5070ti card exists.
Or, it will acknowledge that it made a mistake and continue to make the same mistake again.
I asked Claude to generate an HTML page about PowerShell 7.
It gave me a page saying 7.4 was the latest LTS release.
I corrected it with links showing 7.6 was released in March
and asked it to regenerate with the latest information.
It generated basically the same page with
the same claim that 7.4 was the latest release.
> Or, it will acknowledge that it made a mistake and continue to make the same mistake again.
People do this too though. At least the AI generally tries to follow instructions that you give it even when you are lacking clarity in the details.
I feel like it's similar to the self-driving car problem. The car could have 99.9999% reliability, drive much better and safer than a human, yet folks will still freak out about a single mistake that's made even though you have actual humans today driving the wrong way down the highway, crashing in to buildings, drunk driving, stealing cars, and all sorts of other just absolutely stupid things.
We need to move away from this idea that because it's an AI system it should give you perfect responses. It's not a deterministic system and it can be wrong, though it should get better over time. Your Google search results are wrong all the time too. The NYT writes things that are factually incorrect. Why do we have such a high standard for these models when we don't apply them elsewhere?
it should be reasonably expected that you can give a source and fix an error in the AI output.
I would even go as far as to say if a human directly told the AI "no, use 7.6 as the latest version", the AI should absolutely follow direct instructions no matter what it thinks is true. What if this human was working on a slide about the upcoming release of 7.6 that has no public documentation?
LLMs are (broadly-speaking) poorly-positioned to give you a strong verdict on plausibility of a frontier topic. That said - ChatGPT was exactly right in its response to OP!
"Very deep", "border-line impractical" "in a research-sense" is the perfect summary of this article itself! :)
Watching the entire economy of a superpower and ~all of online culture go absolutely ga-ga over Furbys has been one of the weirdest things I've ever witnessed.
At least ChatGPT is now aware that Codex exists. I have a chat, still in my history, from a few months ago, in which I asked for help wrangling npm to get @openai/codex working, and ChatGPT said:
> Important: Codex CLI no longer exists
> OpenAI discontinued the Codex model + CLI a while back. There is no official binary named codex in any current OpenAI npm packages. OpenAI’s current CLI tool is:
npm install -g openai
> which installs the openai command, not codex.
The world knowledge of these models is not necessarily up to date :)
edit: I replayed the same prompt into current ChatGPT and it is less clueless now. Maybe OpenAI noticed that it was utterly dumb that GPT-5.whatever didn't believe that Codex existed and fine-tuned it.
>The world knowledge of these models is not necessarily up to date :)
It's amazing how this still needs to be said. Codex was released in April 2025. The initial GPT-5 and 5.1 still had a knowledge cutoff in late 2024. Like, what did you expect? Always beware the knowledge cutoff for LLMs (although recent releases have gotten much better with researching the web for updates before answering modern software topics).
Yeah, the solution was to link it to the nvidia page of the card, then it was like, 'oh, okay.' But at that point, I lost faith in it's ability to provide me with the information I was looking for. If it's information is so out of date that it doesn't know about the 5000 series, how could I be confident that it knew the details I was asking about (game engine related research)?
This is pretty impressive. My impression was that eGPUs simply do not work with Apple Silicon.
(EDIT: Apple agrees with my impression. “To use an eGPU, a Mac with an Intel processor is required.” And, on top of that, the officially supported eGPUs were all AMD not NVIDIA. https://support.apple.com/en-us/102363)
This seems pretty useful for AI inference if it can pass Apple approval. I've wanted to use my Nvidia GPUs with a Mac Mini, this would enable it to run CUDA directly. Very cool!
Steam deck runs a full x86-64 AMD APU. The work valve has done for that was to get Windows games to run seamlessly on Linux.
Hopefully in 2026 the Valve Index VR headset which is ARM (Qualcomm?) we get what you're talking about here - basically proton for Win32/64 to Linux ARM64.
Side note that Windows on ARM isn't bad just that its priced out of its league and cooling is awful for gaming on current laptops. The only issue I had was OpenGL needing some obscure GL on DirectX thing for Maya3D to get games to work.
To keep the chain of Cunningham's Law going, Valve's 2026 headset is called the Steam Frame, not the Index (which came out in 2019).
But Valve's ARM efforts even mean that Android devices can play some (mostly less graphically intensive) Steam games. That makes me very excited about the prospects for the future of gaming handhelds.
Wait, why? This is exactly what I as a human would have said in this situation.
Or if you're referring to how the OP still decided to go ahead, I've seen AIs go ahead on impractical courses of action many times, and surprisingly succeed on some of them.
Sadly, as you can tell, they have not taken me up on my requests. Awesome that other people got it working!
Even if the drivers loaded, they can't talk to the GPU from within docker (unless one implements PCI passthrough). MacOS owns the PCI bus in this scenario.
Anyway, the Mac Pro is dead now. There's only so much sales audio and video professionals can provide.
I don’t know about that. Apple supported some full size GPUs in past product lines and the number of users was very small. Granted, LLMs change that demand but the audience for Mac Pro buyers who would use a full-size GPU that is impossible to obtain is almost nothing compared to their laptop sales.
Part of the reason the new Mac Pro failed to find an audience can definitely be blamed on macOS' hostility to third party hardware. Who knows what Apple would be worth if they beat Nvidia's Grace CPU to the datacenter market. It was certainly their opportunity.
The game benchmarks are fun but the LLM improvements are where this gets really interesting for practical use. I love Apple platforms as an approachable way to run local models with a lot of RAM, but their relatively slow prompt processing speed is often overlooked.
> Here you can see the big issue with Macs: the prompt processing (aka “prefill”) speed. It just gets worse and worse, the longer the prompt gets. At a 4K-token prompt, which doesn’t seem very long, it takes 17 seconds for the M4 MacBook Air to parse before we even start generating a response. Meanwhile, if you strap the eGPU to it, it’ll only take 150ms. It’s 120x faster.
The prefill problem goes unnoticed when you’re playing around with the LLM with small chats. When you start trying to use it for bigger work pieces the compute limit becomes a bottleneck.
The time to first token (TTFT) charts don’t look bad until you notice that they had to be shown on a logarithmic scale because the Mac platforms were so much slower than full GPU compute.
Or, more likely, it will tell you something it doesn't know.
Reminds me of yesterday, when I was arguing with ChatGPT that the 5070TI was an actual video card. It kept trying to correct me by saying I must have meant a 4070ti, since no such 5070ti card exists.
I asked Claude to generate an HTML page about PowerShell 7. It gave me a page saying 7.4 was the latest LTS release. I corrected it with links showing 7.6 was released in March and asked it to regenerate with the latest information.
It generated basically the same page with the same claim that 7.4 was the latest release.
People do this too though. At least the AI generally tries to follow instructions that you give it even when you are lacking clarity in the details.
I feel like it's similar to the self-driving car problem. The car could have 99.9999% reliability, drive much better and safer than a human, yet folks will still freak out about a single mistake that's made even though you have actual humans today driving the wrong way down the highway, crashing in to buildings, drunk driving, stealing cars, and all sorts of other just absolutely stupid things.
We need to move away from this idea that because it's an AI system it should give you perfect responses. It's not a deterministic system and it can be wrong, though it should get better over time. Your Google search results are wrong all the time too. The NYT writes things that are factually incorrect. Why do we have such a high standard for these models when we don't apply them elsewhere?
it should be reasonably expected that you can give a source and fix an error in the AI output.
I would even go as far as to say if a human directly told the AI "no, use 7.6 as the latest version", the AI should absolutely follow direct instructions no matter what it thinks is true. What if this human was working on a slide about the upcoming release of 7.6 that has no public documentation?
"Very deep", "border-line impractical" "in a research-sense" is the perfect summary of this article itself! :)
> Important: Codex CLI no longer exists
> OpenAI discontinued the Codex model + CLI a while back. There is no official binary named codex in any current OpenAI npm packages. OpenAI’s current CLI tool is:
> which installs the openai command, not codex.The world knowledge of these models is not necessarily up to date :)
edit: I replayed the same prompt into current ChatGPT and it is less clueless now. Maybe OpenAI noticed that it was utterly dumb that GPT-5.whatever didn't believe that Codex existed and fine-tuned it.
It's amazing how this still needs to be said. Codex was released in April 2025. The initial GPT-5 and 5.1 still had a knowledge cutoff in late 2024. Like, what did you expect? Always beware the knowledge cutoff for LLMs (although recent releases have gotten much better with researching the web for updates before answering modern software topics).
(EDIT: Apple agrees with my impression. “To use an eGPU, a Mac with an Intel processor is required.” And, on top of that, the officially supported eGPUs were all AMD not NVIDIA. https://support.apple.com/en-us/102363)
Hopefully in 2026 the Valve Index VR headset which is ARM (Qualcomm?) we get what you're talking about here - basically proton for Win32/64 to Linux ARM64.
Side note that Windows on ARM isn't bad just that its priced out of its league and cooling is awful for gaming on current laptops. The only issue I had was OpenGL needing some obscure GL on DirectX thing for Maya3D to get games to work.
But Valve's ARM efforts even mean that Android devices can play some (mostly less graphically intensive) Steam games. That makes me very excited about the prospects for the future of gaming handhelds.
"no - not in any practical sense today, and "maybe" only in a very deep, borderline-impractical research sense."
This is why humans will always rule over crappy LLMs.
Or if you're referring to how the OP still decided to go ahead, I've seen AIs go ahead on impractical courses of action many times, and surprisingly succeed on some of them.
Congrats! Each one got what they wanted :).
Unfortunately, I also believe that market forces may push away from this direction, as LLM companies try to capture the value stream
Never let an AI tell you that you cannot do something practical for your own self for research, discovery or for fun.
The only thing that is close to impractical is expecting your non-technical friends or others to follow you without any incentive or benefit.
It’s these people, not the ones who refuse to use LLMs, who are as they say, “cooked”.