[@ServeTheHomeVideo] Supermicro NVIDIA HGX B200: Live Demo Using Multimodal AI

Link: https://youtu.be/wl_e0POozZA

Short Summary

Number One Action Item/Takeaway:

Utilize Llama 4, specifically the Scout version, on the Super Micro B200 system to leverage its multi-modal capabilities, long context window, and integration with Slack for team collaboration and efficient information retrieval from large documents and diverse data types.

Executive Summary:

The demo showcases the power of the Super Micro B200 system running Llama 4 (Scout version) for real-time interaction via Slack. This setup allows team members to ask questions, analyze images, summarize large documents like PDFs (e.g., product data sheets, entire books), and generate content, demonstrating the model's multi-modal capabilities and the system's high performance with long context processing.

Key Quotes

Here are four direct quotes from the YouTube video transcript that I found particularly insightful or interesting:

1. "So the system were equipped with eight black well but we only using four to running this demo and it's a full resistance." - This provides specific hardware configuration information and indicates the system's capability can be scaled.
2. "Mixture of experts. Yes. Okay. Can you elaborate on what that is? Yeah. So they the model is still using a couple layer in transformer but they do have new layer that you can be very flexible. They having active parameter running so they can utilize the power of the model better and you can have a bigger model uh with more hundreds of parameters. And one more thing the lama for also doing is uh they having the big context lane up to like 10 millions of context." - This details a key architectural improvement in Llama 4.
3. "So with the average throughput is about 30 second uh 30 token per second and they will tell that how many KB cache uses every fist cache hit rate also right so as you see here with the llama for model it's a 100 billion parameter so it take about like 200 gabyte to load the model full and then the remain of the memory we'll be using for uh serving the model and I using four core um of the black Well, so you see based on the KV cash usage that they telling me, it's not much of the usage they're using for it to understood." - This provides concrete performance metrics and resource utilization data.
4. "So there's three different kind of model for lama 4. Mhm. Um so I have scout running on the B200 right now. So scout is a more smaller model compared with the MBA rig. So um and they quite different. So scout they do have like up to 10 million context length while the other one is only 1 millions. Uh scout is about like 100 billion parameter and the one the other one is around 300 billion parameters. Yeah." - This highlights the trade-offs of the Llama 4 model families.

Detailed Summary

Okay, here is a detailed summary of the YouTube video transcript using bullet points, excluding sponsorship announcements:

Key Topics & Overview:

• Demo of Llama 4 on Supermicro B200 System: The video showcases a demo of the Llama 4 model running locally on a Supermicro B200 system, equipped with Nvidia Blackwell GPUs.
• Multi-Modal Capabilities: The demo highlights Llama 4's multi-modal capabilities, including text, image, and PDF processing.
• Slack Integration: The Llama 4 model is integrated into a Slack channel, allowing users to interact with the AI through a bot.
• Blackwell GPU Utilization: The demo uses four of the eight Blackwell GPUs available in the B200 system.
• Mixture of Experts (MoE) Model: The video discusses Llama 4's new Mixture of Experts architecture.

Demo Functionality and Examples:

• Basic Interaction: Users can interact with the bot by mentioning it in Slack channels and asking questions.
• Image Analysis: The bot can analyze images and provide descriptions of what it sees. Example: Identifying ingredients in a pizza picture and creating a recipe.
• PDF Processing: The bot can process PDF documents, extract information, and answer questions based on the content. Example: Analyzing a Supermicro B200 product data sheet and summarizing key benefits.
• Book Summarization: The bot can process lengthy documents, such as a 700-page book (Romeo and Juliet), and provide summaries and character analysis.
• Concurrent User Support: The backend VM enables multiple concurrent users to interact with the Llama 4 model via Slack.
• Contextual Awareness: The bot demonstrates understanding of context, such as referencing a previously uploaded pizza image when asked for a recipe.

Technical Details & Behind the Scenes:

• System Configuration: The B200 system has eight Blackwell GPUs, with the demo using four of them.
• Docker Containerization: The Llama 4 model runs within a Docker container.
• Logging and Monitoring: Logs capture the model's responses and performance metrics.
• Resource Usage: The demo monitors KV cache usage and cache hit rates.
• Parameter Count & Memory: Llama 4 (100B parameter version) requires around 200GB of memory to load.
• Token Processing Speed: The demo shows token processing speeds (e.g. 6,329 tokens per second for Romeo and Juliet).
• Bolt API: The Bolt API is used for communication between the Slack front-end and the backend model. Bolt enables querying Slack information, such as user details.

Llama 4 Model Information:

• Latest Model from Meta: Llama 4 is the latest large language model from Meta.
• Mixture of Experts Architecture: This architecture allows for flexible and efficient use of parameters, enabling larger models.
• Long Context Length: Llama 4 has a large context length (up to 10 million tokens), enabling it to process more data, including images, videos, and lengthy documents.
• Scout Version: The demo utilizes the "Scout" version of Llama 4, which is smaller than other versions but still has a large context length.
• Different Model Variants: There are different versions of Llama 4 with varying parameter counts and context lengths (Scout vs. other versions).