AMD's Hidden $100 Stable Diffusion Beast!
TLDRThe video discusses the rapid advancements in machine learning and the potential for general artificial intelligence within the next five years. It highlights AMD's progress in the supercomputing space, with the AMD Instinct MI25 being a cost-effective option for machine learning tasks. The MI25, available for around $100, can be modified to work with certain setups and can handle tasks like stable diffusion, showcasing AMD's commitment to AI and their partnership with PyTorch. The video also touches on the challenges of cooling and software support for older cards, but emphasizes the potential of AMD's CDNA cards for experimentation and the future of AI applications.
Takeaways
- 🚀 The pace of advancement in machine learning is rapid, with the potential for general artificial intelligence to emerge within the next five years.
- 🎮 While Nvidia often dominates the headlines, AMD is making significant strides, particularly in the supercomputing space.
- 💰 AMD's Instinct MI25 GPUs can be found for around a hundred dollars on eBay, offering a cost-effective entry point for those interested in machine learning.
- 🔬 The MI25, despite being older, is capable of running stable diffusion models and can be flashed with a V BIOS to become a WX 9100, nearly doubling its power limit.
- 🛠️ Modifying the MI25 for optimal performance requires a certain level of technical skill and is not recommended for beginners.
- 🌡️ Cooling is a primary concern when working with the MI25, and custom solutions like 3D printed shrouds and brushless blower motors can help maintain the card's temperature.
- 🔗 AMD has partnered with PyTorch to facilitate machine learning applications, making it easier for Python users to get started.
- 📈 The MI25 is based on the Vega 10 architecture with 16GB of VRAM, which, despite its age, still offers substantial capabilities for machine learning tasks.
- 🔌 The MI25 has a standard GPU style dual 8-pin power connector, making it compatible with many existing systems.
- 📉 The newer AMD Instinct cards like the MI100 and MI200 are gaining more attention, but the MI25 remains a viable option for those willing to put in the effort.
- 🌟 AMD's CDNA line, which includes the MI25, is separate from their gaming-focused RNDA line, and is particularly suited for data center and compute-intensive tasks.
Q & A
What is the potential timeline for achieving General Artificial Intelligence (AGI) according to the video?
-The video suggests that we could see something resembling General Artificial Intelligence within the next five years, which is a timeline that has been discussed since the 1980s but is now believed to be more likely to happen.
Why is AMD gaining attention in the machine learning space?
-AMD is gaining attention because it is catching up fast in the machine learning space, particularly with its supercomputer offerings like the AMD stack used by Oak Ridge. The AMD Instinct MI25, despite being an older model, offers significant value for machine learning tasks.
What is the AMD Instinct MI25's price point on eBay?
-The AMD Instinct MI25 can be found for about a hundred dollars on eBay, as it is no longer desired by the top percentile of data center users who are now opting for more expensive GPUs.
How can the AMD Instinct MI25 be adapted for use in a machine learning setup?
-The AMD Instinct MI25 can be flashed with a V BIOS to become a WX 9100. It also has a single Mini DisplayPort output which works with that BIOS. With the right cooling and power limit adjustments, it can be used effectively for machine learning tasks.
What is the VRAM capacity of the AMD Instinct MI25?
-The AMD Instinct MI25 has 16 gigabytes of VRAM, which is substantial for machine learning tasks, even though some models require up to 40 gigabytes of VRAM.
What is the bandwidth of the AMD Instinct MI25?
-The memory bandwidth of the AMD Instinct MI25 is 462 gigabytes per second, which allows for significant processing capabilities in machine learning applications.
What is the challenge with using the AMD Instinct MI25 for machine learning?
-The main challenge with using the AMD Instinct MI25 for machine learning is cooling. It requires a custom cooling solution, which can be tricky to set up and is not recommended for beginners.
What is the potential performance of the AMD Instinct MI25 with stable diffusion models?
-The AMD Instinct MI25 can run stable diffusion models at a resolution of 768x768, achieving about 2.56 to 2.57 iterations per second at that resolution, and only using 12 gigabytes of VRAM.
What is the significance of the AMD partnership with PyTorch for machine learning?
-AMD's partnership with PyTorch means that if you use Python for machine learning, you can easily integrate AMD's hardware into your setup, making it more accessible and user-friendly.
What is the current state of software support for the AMD Instinct MI25?
-The AMD Instinct MI25 is on the edge of software support as AMD continues to add new features and updates to their newer Instinct line of products. However, it can still be used effectively with the right modifications and software setup.
What is the future outlook for AMD in the field of AI and machine learning?
-AMD is expected to continue supporting AI with proper ROCm support for their 7000 series GPUs and beyond, offering more powerful options like 20GB and 24GB VRAM GPUs. This shows a commitment to remaining competitive in the AI and machine learning space.
What is the ultimate goal of the project discussed in the video?
-The ultimate goal of the project is to create a system where an AI agent can substitute in favorite actors and characters into any movie or genre, allowing for the creation of custom mashups or memes.
Outlines
🚀 Advancements in AI and Machine Learning Hardware
The first paragraph discusses the rapid progress in the field of machine learning and the potential for achieving General Artificial Intelligence within the next five years. It touches on the challenges of experimenting with this technology, mentioning the use of gamer GPUs and the limitations they have in terms of VRAM. The speaker highlights the competition between Nvidia and AMD in the supercomputer space, with AMD making significant strides. The discussion then shifts to repurposing older AMD hardware like the Instinct MI-25 for machine learning tasks, which can be found at a fraction of the cost of newer GPUs. The paragraph also covers the process of modifying these older GPUs to work with newer software and the potential savings and performance one can achieve, especially when dealing with tasks like stable diffusion and automatic image generation.
🤖 AI's Growing Capabilities and Hardware Support
The second paragraph continues the discussion on AI, focusing on the support AMD provides for the PyTorch foundation and AI in general. It reflects on a past Halloween video project and the current capabilities of AI to generate humorous and interesting content, such as replacing characters in movies with a specific actor. The speaker anticipates that AI will reach a level where it can perform such tasks much sooner than expected. The paragraph also mentions the potential of using older hardware like the Instinct MI-25 for these tasks, emphasizing the importance of software advancements. It concludes with a teaser for a future video on progress with VFIO GPU pass-through and a nod to the community member 'gigabuster' for their contributions to the forum.
Mindmap
Keywords
💡Machine Learning
💡AMD
💡Instinct MI-25
💡VRAM
💡Stable Diffusion
💡GPU
💡Python
💡Cooling
💡General Artificial Intelligence (AGI)
💡eBay
💡Vega 10
Highlights
The potential for General Artificial Intelligence (AGI) to emerge within the next five years is discussed.
AMD is rapidly catching up in the machine learning space, despite Nvidia's dominance.
AMD's Instinct MI-25 GPUs can be found for around a hundred dollars on eBay, offering significant value for machine learning tasks.
The Instinct MI-25 can be flashed with a V BIOS to become a WX 9100, enhancing its capabilities.
With the right cooling solutions, the MI-25 can handle high power limits and remain stable for machine learning applications.
The MI-25 is based on the Vega 10 architecture with 16GB of VRAM, suitable for various machine learning models.
The MI-25's memory bandwidth is 462 gigabytes per second, allowing for substantial machine learning tasks.
Stable diffusion models can run on the MI-25, providing high-fidelity previews for various applications.
The MI-25 has dual 8-pin power connectors and is compatible with standard GPU style connectors.
Custom cooling solutions, such as modified NZXT brackets and 3D printed shrouds, can be used to enhance the MI-25's performance.
The MI-25 can run stable diffusion models at 768x768 resolution, showcasing its competence in AI tasks.
AMD's partnership with PyTorch facilitates easy setup and use for machine learning with Python.
The MI-25's performance is impressive for its price, offering 16GB of HBM2 for machine learning at a hundred dollars.
AMD is working on improving support for their 7000 series GPUs and beyond, with higher VRAM capacities.
The transcript discusses the future of AI and its potential to create personalized content, such as replacing characters in movies with AI-generated actors.
The MI-25's capabilities are demonstrated through a guide on setting up Open Assistant with an open source model.
The MI-25 is part of AMD's CDNA line, which is distinct from their gaming-focused RNDA line.
The MI-25's value proposition is emphasized, highlighting its potential for experimentation and machine learning applications.