AMD's Hidden $100 Stable Diffusion Beast!
Summary
TLDRThis video explores the rapid advancements in machine learning and the potential emergence of General Artificial Intelligence within five years. It discusses the use of AMD's Instinct MI-25 GPUs for machine learning tasks, highlighting their cost-effectiveness and compatibility with PyTorch. The script also covers the process of repurposing these GPUs for stable diffusion models and the challenges of cooling and software support. The presenter shares insights on building powerful systems for AI, emphasizing the progress and capabilities of current hardware in facilitating AI advancements.
Takeaways
- 🚀 The rapid advancement in machine learning could lead to the emergence of General Artificial Intelligence (AGI) within the next five years.
- 🎮 Gamer GPUs can be utilized for machine learning tasks, although they may lack the VRAM compared to professional GPUs.
- 🔍 AMD is making significant strides in the supercomputer space, challenging Nvidia's dominance with competitive offerings.
- 🛍️ Instinct MI-25 GPUs can be found for around a hundred dollars on eBay, offering a cost-effective entry point for those looking to experiment with machine learning.
- 🔧 With some effort, Instinct MI-25s can be flashed with a V BIOS to become WX 9100s, enhancing their capabilities for machine learning tasks.
- 🔄 AMD has been continuously updating its software to support its Instinct line of GPUs, which is crucial for running modern machine learning models.
- 🛠️ Cooling solutions are a significant challenge when repurposing older GPUs like the MI-25 for machine learning applications.
- 💻 The script mentions a DIY approach to cooling and modifying hardware for better performance in machine learning tasks.
- 🌐 AMD's partnership with PyTorch facilitates easy setup for Python-based machine learning projects on their GPUs.
- 📈 The potential of older hardware like the MI-25 is highlighted, showing that it can still perform competently for tasks like stable diffusion.
- 🔮 The script speculates on the future of AI, suggesting that we may soon have personal AI assistants that are indistinguishable from AGI.
Q & A
What is the current pace of development in the machine learning space according to the transcript?
-The transcript suggests that the pace of development in the machine learning space is very fast, with the possibility of seeing General Artificial Intelligence (AI) or something resembling it within the next five years.
Why does the speaker believe that we might see General AI sooner than expected?
-The speaker believes that the current advancements and the rate of progress in hardware and software development, as well as the increasing support for AI, indicate that General AI might be achieved sooner than previously anticipated.
What are the challenges of using gamer GPUs for machine learning?
-Gamers GPUs can be used for machine learning, but they may lack sufficient VRAM, which is necessary for handling large datasets and complex models in machine learning.
Why is AMD catching up fast in the supercomputer space according to the transcript?
-AMD is catching up fast due to their competitive offerings and their presence in significant projects like the Oak Ridge supercomputer, which uses the AMD stack.
What is the significance of the Instinct MI-25s in the context of the discussion?
-The Instinct MI-25s are significant because they offer a cost-effective option for machine learning with 16GB of VRAM, and with some modifications, they can be repurposed to work with newer systems.
What modifications can be done to an Instinct MI-25 to enhance its capabilities?
-An Instinct MI-25 can have its V BIOS flashed to become a WX 9100, which can almost double the power limit of the card, provided it can be kept cool with appropriate cooling solutions.
Why is the AMD partnership with PyTorch important for machine learning?
-The partnership with PyTorch is important because it allows for seamless integration of AMD's hardware with Python-based machine learning frameworks, making it easier for developers to start working on machine learning projects.
What is the role of the 3D printable shroud in the context of the script?
-The 3D printable shroud is used to create a cooling solution for the Instinct MI-25 GPU. It allows for the attachment of a brushless blower motor to help dissipate heat more effectively.
What is the significance of the mi-25's dual 8-pin power connectors?
-The dual 8-pin power connectors on the mi-25 are significant because they are standard GPU style connectors, making it easier to integrate the card into existing systems.
What is the current status of AMD's support for AI and machine learning?
-AMD is actively supporting AI and machine learning by partnering with frameworks like PyTorch, developing new software and features for their Instinct line of products, and working on proper ROCm support for their 7000 series GPUs and beyond.
What is the potential future application of AI as mentioned in the transcript?
-The transcript suggests a future where AI can be used to create personalized content, such as substituting actors and characters in movies or creating custom mashups and memes.
Outlines
🚀 Advancements in AI and GPU Technology
The script discusses the rapid progress in machine learning and the potential for General Artificial Intelligence (AI) to emerge within the next five years. It touches on the use of gamer GPUs for AI experiments due to their limitations in VRAM and compares the performance and market presence of Nvidia and AMD in the supercomputer space. AMD's Instinct MI-25 GPUs are highlighted as a cost-effective option for those willing to invest time in setup, with the potential to be repurposed for AI tasks. The script also mentions the partnership between AMD and PyTorch for machine learning, and the capabilities of older hardware like the Instinct MI-25 for running AI models, despite being on the edge of software support.
🎨 AI's Creative Potential and Hardware Considerations
This paragraph delves into the creative applications of AI, such as generating images of characters like Danny DeVito in various scenarios, showcasing AI's ability to perform tasks once thought to be years away. It emphasizes the support AMD provides for PyTorch and AI development, and the potential for AI to serve as a personal assistant indistinguishable from General AI. The script also discusses the challenges of using older hardware like the Radeon Pro V540 for AI tasks and the progress being made in GPU pass-through technology. It concludes with a look at the Instinct MI-25's capabilities for running AI models like Stable Diffusion, and the importance of cooling solutions for maintaining performance.
Mindmap
Keywords
💡General Artificial Intelligence (AGI)
💡Machine Learning
💡GPUs (Graphics Processing Units)
💡VRAM (Video Random Access Memory)
💡Nvidia and AMD
💡Instinct MI-25
💡PyTorch
💡Stable Diffusion
💡HBM2 (High Bandwidth Memory 2)
💡VFIO (Virtual Function I/O)
💡CDNA (Compute DNA)
Highlights
The rapid advancement in machine learning could lead to the emergence of General Artificial Intelligence within the next five years.
Experimentation with hardware for AI can be tricky due to limitations like VRAM, with options like using gamer GPUs or piecing other components together.
AMD is catching up fast in the supercomputer space, with Oak Ridge using the AMD stack for their operations.
Instinct MI-25s, once used by the one percent, can now be found on eBay for around a hundred dollars and repurposed for AI tasks.
AMD has partnered with PyTorch for ease of use in machine learning with Python.
With some effort, an Instinct MI-25 can be flashed with a V BIOS to become a WX 9100, almost doubling its power limit.
The MI-25, despite being older, still offers 16 gigabytes of VRAM and can handle machine learning tasks effectively.
Stable diffusion models can run on the MI-25, providing high-fidelity previews in a reasonable time frame.
The MI-25 has dual 8-pin power connectors and uses a standard GPU style connector, making it compatible with existing systems.
Cooling is a significant challenge when repurposing enterprise cards like the MI-25 for AI tasks.
A 3D printable shroud and brushless blower motor can be used to cool the MI-25 effectively in standard cases.
Stable diffusion is capable of running 768x768 models on the MI-25, demonstrating the card's surprising competence.
The MI-25's performance is impressive for its price, especially when considering its 16GB HBM2 VRAM.
AMD's focus on supporting PyTorch and AI in general is driving the development of new software and features for their Instinct line.
The potential for AI to replace characters in movies with AI-generated images, like Danny DeVito, is closer than expected.
The hardware available today is capable of running the software that will enable AI agents to perform complex tasks.
The Radeon Pro V540, while not ideal for machine learning, represents an opportunity for experimentation with VFIO GPU pass-through.
AMD's CDNA and RDNA are separate lines, with CDNA being more focused on data centers and compute tasks.
The project demonstrates the intersection of hardware experimentation, 3D printing, and machine learning for creative AI applications.
Transcripts
[Music]
foreign
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things are moving so fast in the machine
learning space that we could actually
see General artificial intelligence or
at least something that resembles
General artificial intelligence within
the next five years like I know we've
been saying that since the 80s or
certain people have been saying that
since the 80s but maybe it's actually
really happening this time I don't know
if you want to experiment with this no
it gets a little tricky you can use
gamer gpus but you don't have a lot of
vram or you can try to piece other
things together Nvidia gets all the
attention but AMD is actually catching
up fast but make no mistake they've
always been there in the super computer
space I mean there's a reason that Oak
Ridge is using the AMD stack for all of
their stuff but those are the smartest
guys in the room and sometimes it's
exhausting being the smartest guys in
the room so what do you do well the
Instinct mi-25s are about a hundred
dollars on eBay because the one
percenters don't want those anymore they
don't want those in the data center
they're busy buying forty thousand
dollar gpus or twenty five thousand
dollar gpus systems like the mi2 10. I
took a part with Gamers Nexus and we did
some builds our super micro big twin
system six mi210s and 2u that is an
absolutely ridiculous system an AMD for
their part they partnered with pi torch
so if you use Python for machine
learning or anything like that you can
drop in and you're ready to go it's a
little bit more of an uphill battle
getting an instinct mi-25 to work with
that setup but if you're willing to put
in the work a hundred dollars for an
instinct mi-25 you can Flash the V bios
on it to be a WX 9100 and it does
actually have a single Mini DisplayPort
out which will work with that bios you
can almost double the power limit of the
card and as long as you can keep it cool
with whatever Madness you happen to be
running
it will actually be pretty stable now
gigabuster on our forum is the one that
put this together and figured it out and
the dependencies and all of the software
see the mi-25s are so old they're right
on the edge of software support and AMD
has been adding new software and new
features and new everything for their
Instinct line for you know like the
mi-100 and the mi-200 and now the Mi 300
were on the precipice of that and so
those are the cards that are getting the
most attention the mi-25 is based around
the Vega 10 so that's gcn 5.0 but it has
16 gigabytes of vram 16 gigabytes of
vram yes the membrane bandwidth is 462
gigabytes per second you can do a lot
with that with machine learning even
though 16 gigabytes I mean some of these
models take like 40 gigabytes of vram
but you can still do a lot of stable
diffusion stable diffusion automatic 111
running in your local browser doing your
own stuff you can get a bunch of
previews I mean it takes like 20 minutes
to get 16 previews at very high fidelity
768 I'll show you it's it's worth it I
promise and the Mi 25
has dual 8-pin power connectors and is
fortunately a standard GPU style
connector sometimes the CPU 8-pin and
the GPU 8-pin the Enterprise cards a lot
of the time will have a CPU style 8-pin
connector which is a different wiring
than a GPU style 8-pin connector but
these two have the seat the GPU style
eight pin connector so it's pretty easy
to hook up in an existing system the
biggest problem is cooling so we've got
the NZXT bracket here that we've
modified a little bit getting the GPU
mounting pressure just right when you do
this is a little tricky definitely not
recommended not for the faint of heart
and probably not your first project in
an Ideal World the more accessible
solution is to download this 3D
printable shroud and uh bum somebody's
3D printer if you don't have one it'll
Mount here on the end of your card and
then you can pick up a standard you know
this is a
bfb1012h brushless blower motor that's
three pin so it's wired for your
motherboard and then boom look at that
now this is the longest GPU ever but
this will work in cases such as the
fractal meshify the big one and as long
as this fan is running at Full Tilt you
can run
170 Watts through this card without too
much issue now stable diffusion is a lot
of fun and you can run 768 by 768 models
with this it's it's actually
surprisingly competent so at floating
Point 32 512 by 512 with the Euler 20
step it's about uh it's 2.56 to 2.57
iterations per second at 768 by 768 it's
more like 27 seconds so not bad but it
is only using 12 gigabytes of vram so
you're staying well under the 16
gigabyte limit for comparison for how
far we've come that super micro big twin
system if you didn't see those videos be
sure to check out those videos floating
Point 32 20 Step 2 seconds for 512 by
512 and 6 seconds for 768 by 768. that's
pretty fast and so once you follow the
guide and get everything up and running
it works really well now if you're using
newer Hardware you don't really have to
worry about the versions as much again
because AMD is supporting the pi torch
foundation and because they're
supporting AI in general and because you
know it's it's it's it's amd's coming we
did this fun clip of The Shining for a
video that we released on Halloween last
year and it's even more like that today
I've got this thing running generating
you know fun interesting Danny DeVito
images because if you've watched level
one for a long time you know our
Benchmark for AI is when we get to an AI
agent that's just here is the Lord of
the Rings movies from Peter Jackson I
would like for you to replace every
character in this movie with Danny
DeVito and we're basically at the point
where AI can do that
a lot sooner than I expected so that's
why I say artificial general
intelligence probably coming a lot
sooner than I expected and probably on
the order of five years or so or at
least you can have a personal assistant
that is indistinguishable from General
artificial intelligence maybe I don't
know we'll see because you can do this
on an mi-25 it's the software that's
catching up the hardware that we have
today is what's going to run that that's
probably why people are buying these
gpus for 25 35 45
000 even on eBay well the newer ones not
the mi-25s these are these are you know
a hundred dollars oh actually this is
the Radeon Pro v540 Amazon is getting
rid of these right now this is not the
kind of GPU that you will want to do
this stuff on but this is a dual GPU
solution Amazon used to have these you
can get your hands on these as well this
is going to be a different video though
these are maybe not for machine learning
and it's a little tricky to get the
drivers for that if you can help with
the windows drivers for this because
they're in the Amazon Cloud and that's
pretty much it they're not on the AMD
website because this is a you know v540
which is a dual
version of another AMD GPU so it's a
little weird but maybe is a good
candidate for our vfio GPU pass-through
stuff which by the way is making a lot
of progress gonna look out for a video
on that soon yeah the Instinct Mi 25 for
100 that you're able to do this at a
reasonable speed genuinely very
impressive and yeah you can do it on a
gamer GPU but 16 gigabytes of hbm2 for a
hundred dollars again that's a really
good deal I don't know that I would pay
a lot more than 100 because you will put
in a lot of work in order to get it
actually working and follow the level
one guide again thanks gigabuster but uh
yeah you can you can build kind of a
beastly machine assuming that you can
keep them cool stable diffusion on AMD
Hardware both old and new
basically ready
shockingly good and it's a preview for
what's next I've also written a little
guide on getting open Assistant working
with one of their open source models
yeah there's a model that's really good
but it's sort of encumbered by some
licensing issues for commercial and
other use but they do have fully open
models so you can download one of the 12
billion parameter models there and be
able to run it but you will need a beefy
GPU it out of memories uh even with 16
gig vram
gpus and AMD is working on proper Rock M
support for 7000 series gpus and Beyond
so 20 gigs 24 gigs but just understand
the AMD has their cdna and their rdna
and those are separate lines these are
cdna cards compute DNA and that's what
they still have in the data center
that's what our mi210 is that's what the
Mi 300s are and eventually those roads
may come back together but fundamentally
your cdna and your uh your your gaming
cards are a different things and so it's
a little it's there for experimentation
but it's it's a little different this
has been a project I mean where else can
you play with the angle grinder and 3D
printed parts and also machine learning
toward our ultimate goal of being able
to just ask an AI agent to substitute in
your favorite actors and characters into
whatever movie and genre you want to
create any kind of mashup or meme that
you want much to the horror of literally
everybody that's not a normal human
being I'm one of those level one this
has been some fun
with the AMD Instinct mi-25 and to show
that you know if you're just going to
run pytorch you're basically good to go
on an AMD cdna cards at this point and
it's very good there's a reason that Oak
Ridge is using this
and whether this level one I'm signing
out you find me in the level one forms
foreign
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