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Added notes on evalution topics of Scalability, Clustering, and the concept of machine/node roles [compute, AI, storage, network] #1

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parkingmeter commented 2024-06-22 20:17:14 -06:00
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Just a few quick additions, let me know if you have questions.

I like the Beelink SER7 7840HS box, I'm interested to know how it would fit in this evaluation.

The custom build box would likely include high-speed networking with a PCI expansion card to be competitive with the aggregated network evaluations of the multi-node alternatives.

Just a few quick additions, let me know if you have questions. I like the [Beelink SER7 7840HS]([url](https://www.bee-link.com/products/ser7-7840hs-1)) box, I'm interested to know how it would fit in this evaluation. The custom build box would likely include high-speed networking with a PCI expansion card to be competitive with the aggregated network evaluations of the multi-node alternatives.
datawarrior was assigned by parkingmeter 2024-06-22 20:17:14 -06:00
parkingmeter added 1 commit 2024-06-22 20:17:15 -06:00
Added notes on evalution topics of Scalability, Clustering, and the concept of machine/node roles [compute, AI, storage, network] a4b8515b09
datawarrior commented 2024-06-23 00:07:29 -06:00
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The Beelink SER7 7840H was one of my first choices. If we don't need to worry about graphics cards or trying to add an external GPU, it is a great price for the computing power that it has. The problem is that the Beelink isn't compatible since it doesn't have the right USB/thunderbolt for this expansion, from what I understand. Which is little, so... happy to explore. I added the network metrics (which I don't think I scaled appropriately; I think it needs more weight). The MINISFORUM UM690S Mini had higher speed network capabilities and a MUCH better-integrated graphics card that could be utilized for some small models. It may be a mix. The Beelink is almost identical to the two mini's I have now. I am just about to deploy that one on my proxmox cluster and start seeing how it performs. Over all, I think a combination of these two systems might be a good something to look into.

Ultimately, Comparing the custom build that I did with the just-used parts from data centers that I found on eBay shows it is probably more affordable and efficient for us to use the Mini PC options unless a server falls into our lap. The power consumption was out of scope for this, but I'm pretty sure it wouldn't be hard to prove how much more energy a server would run over these little boxes I think they'll run around 2 Watts while idle, and if you start to over-clock them or push them, you might get somewhere around 30 or 40 Watts which is still less than a standard light bulb.

FYI - External GUP thoughts???

Options for External GPUs:

  1. eGPU (External GPU) Enclosures:

    • Description: eGPU enclosures allow you to connect a desktop GPU to a laptop or mini PC using a high-speed interface like Thunderbolt 3 or USB-C. This setup provides the benefits of a dedicated GPU without needing a large desktop case.
    • Compatibility: Check if the KAMRUI Mini PCs have a Thunderbolt 3 or USB-C port with support for eGPU. If so, you can use enclosures like the Razer Core X or Akitio Node.
    • Example:
      • Razer Core X: Compatible with most modern GPUs, provides ample power supply, and has good thermal management.
      • Akitio Node: A more budget-friendly option, also compatible with a wide range of GPUs.

  2. Networked GPUs (Distributed GPU System):

    • Description: This involves setting up a networked system where multiple computers share a centralized GPU resource. This is more complex but allows for powerful distributed computing.
    • Software Solutions:
      • NVIDIA vGPU: Allows multiple virtual machines to share the power of a single GPU. Useful for setting up a virtualized environment with GPU capabilities.
      • Distributed Machine Learning Frameworks: Use frameworks like TensorFlow with distributed computing capabilities to leverage multiple CPUs and GPUs across different machines in your network.
    • Hardware Solutions:
      • DGX Systems: NVIDIA DGX systems are designed for AI and machine learning workloads and can be shared across a network.

Implementation Examples:

  1. eGPU with KAMRUI Mini PCs:

    • Setup: Connect an eGPU enclosure to each KAMRUI Mini PC via Thunderbolt 3 or USB-C.
    • Use Case: Ideal for individual AI model training or inference tasks where each mini PC can leverage the external GPU for enhanced performance.

  2. Distributed GPU System:

    • Setup: Deploy a powerful server with multiple GPUs (e.g., an NVIDIA DGX system) and use networked access for AI workloads.
    • Software: Use Docker or Kubernetes to manage workloads, ensuring that the GPU resources are efficiently utilized by different nodes (KAMRUI Mini PCs) in the network.
    • Use Case: Ideal for large-scale distributed AI model training where tasks can be parallelized across multiple nodes.

Pros and Cons:

eGPU Setup:

  • Pros:
    • Easy to set up and configure.
    • Significant boost in GPU performance.
    • Scalable by adding more eGPU enclosures as needed.
  • Cons:
    • Requires Thunderbolt 3 or compatible USB-C ports.
    • Additional cost for eGPU enclosures and GPUs.

Distributed GPU System:

  • Pros:
    • High scalability and flexibility.
    • Can leverage powerful centralized GPU resources.
    • Suitable for large-scale AI workloads.
  • Cons:
    • More complex setup and management.
    • Requires a high-speed network infrastructure.
    • Higher initial investment for GPU server hardware.
The Beelink SER7 7840H was one of my first choices. If we don't need to worry about graphics cards or trying to add an external GPU, it is a great price for the computing power that it has. The problem is that the Beelink isn't compatible since it doesn't have the right USB/thunderbolt for this expansion, from what I understand. Which is little, so... happy to explore. I added the network metrics (which I don't think I scaled appropriately; I think it needs more weight). The MINISFORUM UM690S Mini had higher speed network capabilities and a MUCH better-integrated graphics card that could be utilized for some small models. It may be a mix. The Beelink is almost identical to the two mini's I have now. I am just about to deploy that one on my proxmox cluster and start seeing how it performs. Over all, I think a combination of these two systems might be a good something to look into. Ultimately, Comparing the custom build that I did with the just-used parts from data centers that I found on eBay shows it is probably more affordable and efficient for us to use the Mini PC options unless a server falls into our lap. The power consumption was out of scope for this, but I'm pretty sure it wouldn't be hard to prove how much more energy a server would run over these little boxes I think they'll run around 2 Watts while idle, and if you start to over-clock them or push them, you might get somewhere around 30 or 40 Watts which is still less than a standard light bulb. ## FYI - External GUP thoughts??? ### Options for External GPUs: 1. **eGPU (External GPU) Enclosures:** - **Description:** eGPU enclosures allow you to connect a desktop GPU to a laptop or mini PC using a high-speed interface like Thunderbolt 3 or USB-C. This setup provides the benefits of a dedicated GPU without needing a large desktop case. - **Compatibility:** Check if the KAMRUI Mini PCs have a Thunderbolt 3 or USB-C port with support for eGPU. If so, you can use enclosures like the Razer Core X or Akitio Node. - **Example:** - **Razer Core X:** Compatible with most modern GPUs, provides ample power supply, and has good thermal management. - **Akitio Node:** A more budget-friendly option, also compatible with a wide range of GPUs. 2. **Networked GPUs (Distributed GPU System):** - **Description:** This involves setting up a networked system where multiple computers share a centralized GPU resource. This is more complex but allows for powerful distributed computing. - **Software Solutions:** - **NVIDIA vGPU:** Allows multiple virtual machines to share the power of a single GPU. Useful for setting up a virtualized environment with GPU capabilities. - **Distributed Machine Learning Frameworks:** Use frameworks like TensorFlow with distributed computing capabilities to leverage multiple CPUs and GPUs across different machines in your network. - **Hardware Solutions:** - **DGX Systems:** NVIDIA DGX systems are designed for AI and machine learning workloads and can be shared across a network. ### Implementation Examples: 1. **eGPU with KAMRUI Mini PCs:** - **Setup:** Connect an eGPU enclosure to each KAMRUI Mini PC via Thunderbolt 3 or USB-C. - **Use Case:** Ideal for individual AI model training or inference tasks where each mini PC can leverage the external GPU for enhanced performance. 2. **Distributed GPU System:** - **Setup:** Deploy a powerful server with multiple GPUs (e.g., an NVIDIA DGX system) and use networked access for AI workloads. - **Software:** Use Docker or Kubernetes to manage workloads, ensuring that the GPU resources are efficiently utilized by different nodes (KAMRUI Mini PCs) in the network. - **Use Case:** Ideal for large-scale distributed AI model training where tasks can be parallelized across multiple nodes. ### Pros and Cons: **eGPU Setup:** - **Pros:** - Easy to set up and configure. - Significant boost in GPU performance. - Scalable by adding more eGPU enclosures as needed. - **Cons:** - Requires Thunderbolt 3 or compatible USB-C ports. - Additional cost for eGPU enclosures and GPUs. **Distributed GPU System:** - **Pros:** - High scalability and flexibility. - Can leverage powerful centralized GPU resources. - Suitable for large-scale AI workloads. - **Cons:** - More complex setup and management. - Requires a high-speed network infrastructure. - Higher initial investment for GPU server hardware.
datawarrior closed this pull request 2024-06-23 00:56:10 -06:00
datawarrior reopened this pull request 2024-06-23 00:56:42 -06:00
datawarrior approved these changes 2024-06-23 01:38:27 -06:00
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datawarrior approved these changes 2024-06-23 01:40:59 -06:00
datawarrior closed this pull request 2024-06-23 01:42:10 -06:00
parkingmeter reopened this pull request 2024-06-23 06:55:14 -06:00
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  • Evaluating Cost-Effective and Scalable Hardware Solutions for Home Lab and Small Data Center_ A Comprehensive Analysis.md
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