Best AI/ML/DL Rig For 2024 - Most Compute For Your Money!

TheDataDaddi
29 Dec 202317:56

TLDRIn this video, the host discusses the best deep learning rig for the money in 2024, advocating for a cost-effective setup using older Dell PowerEdge R720 servers. The recommended configuration includes two 20-core CPUs, 256GB DDR3 RAM, and two Tesla P4 GPUs for a total of 48GB vRAM. The host emphasizes the value of this setup for AI, ML, and DL tasks, comparing it favorably to custom rigs and cloud-based solutions in terms of performance and cost, while also highlighting the flexibility and upgradability of the system.

Takeaways

  • 💰 The speaker suggests that the best deep learning rig for the money in 2024 is an older Dell PowerEdge R720 server, offering good performance at a lower cost.
  • 🚀 The recommended setup includes 40-core CPUs, 256GB DDR3 RAM, and two Tesla P4 GPUs, providing a total of 48GB VRAM for deep learning tasks.
  • 💼 The server comes with 2 x 1.2TB SAS hard drives, which the speaker advises using as a separate virtual drive for booting and redundancy.
  • 🔧 The speaker has paired the server with five 2TB Teamgroup SATA SSDs for a total of 10TB of fast storage for data and projects.
  • 💡 The total cost for the described setup is around $1,000, which the speaker believes offers excellent value for the performance provided.
  • 💸 The monthly operating cost is calculated to be around $50, based on an average power consumption of 3.4 kilowatts and an electricity cost of 12 cents per kilowatt-hour.
  • 🔎 The speaker compares this setup to a custom rig, highlighting that while the custom rig may be faster and more modern, it offers less overall power for the same price.
  • ☁️ Cloud GPU solutions are acknowledged as an option, but the speaker prefers the ease and cost-effectiveness of directly accessing and managing hardware.
  • 🔄 The speaker mentions the flexibility of upgrading the custom rig, such as adding more powerful GPUs or storage, to suit specific needs.
  • 📈 A cost analysis is provided to compare the upfront and ongoing costs of the suggested setup versus custom and cloud-based solutions, emphasizing the value of the older hardware approach.
  • 📢 The speaker invites feedback and questions, and encourages viewers to like, subscribe, and consider supporting the content through likes or buying a coffee.

Q & A

  • What topic is the speaker focusing on in the video?

    -The speaker is discussing the best deep learning rig for the money in 2024.

  • What type of server does the speaker recommend for deep learning?

    -The speaker recommends using Dell PowerEdge R720 servers, which are older but still solid workhorses for deep learning tasks.

  • What are the specifications of the server the speaker purchased?

    -The server has 40 cores in total from two CPUs with 20 cores each, 256 GB of DDR3 RAM at 1600 MHz, and comes with two 1.2 terabyte SAS hard drives.

  • How much VRAM does the setup with Tesla P4s provide?

    -The setup with Tesla P4s provides 48 gigabytes of VRAM in total.

  • What is the approximate cost of the entire setup the speaker recommends?

    -The entire setup costs around $1,250.

  • What is the estimated monthly electricity cost for the setup?

    -The estimated monthly electricity cost is about 50 cents, based on an average power consumption of 3.4 kilowatts and an electricity cost of 12 cents per kilowatt-hour.

  • How does the speaker compare the recommended setup to a custom rig?

    -The speaker compares the recommended setup to a custom rig by highlighting that the custom rig, while faster and newer, offers fewer cores and less RAM for a similar price, making it less powerful for deep learning tasks.

  • What are the speaker's thoughts on cloud-based GPU solutions?

    -The speaker acknowledges that cloud-based GPU solutions are a good option if one has the budget, but prefers the ease and cost-effectiveness of directly accessing and managing hardware.

  • How does the speaker address the age of the hardware used in the recommended setup?

    -The speaker argues that the age of the hardware is less important than the performance it provides, and that the older hardware still offers significant value and capability for deep learning tasks.

  • What alternative options does the speaker mention for those on a budget?

    -The speaker mentions alternatives such as pay-per-compute or hourly services from platforms like Leno or Kaggle, but notes that these may have limitations and may not be as cost-effective in the long run.

  • What is the speaker's final verdict on the best deep learning setup for the money?

    -The speaker firmly believes that the best value for money is achieved by purchasing older hardware and assembling it oneself, as it offers a good balance of performance and cost-effectiveness.

Outlines

00:00

🤖 Optimal Deep Learning Rig for 2024

The speaker introduces the topic of the best deep learning rig for the money in 2024, sharing their opinion based on experience. They discuss using Dell Power Edge r720 servers, which are older but reliable workhorses, especially for those new to deep learning or needing affordable access to resources for large language models or computer vision. The speaker details their recent build, highlighting the server's 40-core CPUs, 256GB DDR3 RAM, and RAID controller. They also mention using two 1.2TB SAS hard drives for a separate virtual drive and boot drive with redundancy. The focus is on性价比 (cost-performance ratio) and the speaker shares their personal setup, including two Tesla P4s for significant compute power and 48GB of VRAM.

05:01

💰 Cost Analysis of the Deep Learning Setup

The speaker provides a cost analysis of the deep learning setup, totaling around $1,000 for the entire configuration. They mention the monthly operating cost, based on 3.4 kilowatts of power consumption and an electricity rate of 12 cents per kilowatt-hour, resulting in approximately 50 cents per day. The speaker challenges the audience to find a better setup for less money and compares this setup to a custom rig, highlighting the significant difference in core count and RAM. They emphasize the value of the server setup for tackling various AI, ML, and DL problems and suggest that while custom rigs may be faster, they are not as cost-effective.

10:03

🚀 Comparing Different Deep Learning Options

The speaker compares different options for deep learning setups, including custom rigs, cloud GPU services, and their recommended server setup. They discuss the specs of the RTX 6000 GPUs in the cloud service, noting the higher cost and limited resources compared to their server setup. The speaker points out that while cloud services offer flexibility, they come with caveats like data transfer limits and higher monthly costs. They advocate for the benefits of owning and managing the hardware, citing ease of access, cost-effectiveness, and the ability to upgrade as needed. The speaker also mentions the possibility of pay-per-compute options for those on a budget but expresses a preference for self-managed hardware for its enduring value.

15:04

🎉 Conclusion and Future Recommendations

The speaker concludes by reiterating their belief in the value of older hardware for deep learning, emphasizing performance over age. They invite questions and comments from the audience and encourage feedback for improvement. The speaker also reminds viewers to like, subscribe, and support their content through buying them a coffee, with the link provided in the video description. They thank the audience for their engagement and look forward to connecting in the New Year.

Mindmap

Keywords

💡Deep Learning

Deep Learning is a subset of machine learning that uses artificial neural networks to model and understand complex patterns in data. In the context of the video, the speaker is discussing the best hardware configurations for running deep learning algorithms, which often require significant computational resources to process large datasets and train models effectively.

💡Performance for the Money

Performance for the money refers to the balance between the cost of a product or service and the quality or capability of that product or service. In the video, the speaker is focused on finding the most cost-effective solution for deep learning, where the hardware provides the best possible performance relative to its price.

💡Dell PowerEdge R720 Servers

Dell PowerEdge R720 Servers are a line of server hardware produced by Dell that are designed for a variety of computing tasks, including deep learning. These servers are mentioned in the video as a cost-effective and reliable choice for building a deep learning rig due to their solid construction and ability to handle the demands of computationally intensive tasks.

💡RAID Controller

A RAID (Redundant Array of Independent Disks) controller is a component that manages multiple hard drives in a RAID configuration, which is a way to store the same data in different places to protect against data loss. In the context of the video, the RAID controller is part of the server setup and contributes to the overall reliability and performance of the deep learning rig.

💡Tesla P4s

Tesla P4s are a line of graphics processing units (GPUs) developed by NVIDIA, specifically designed for use in servers and workstations for deep learning and other compute-intensive tasks. These GPUs offer a high amount of computing power, which is crucial for training neural networks and performing complex calculations quickly.

💡VRAM

Video RAM (VRAM) is a type of memory used to store image data that is being processed by the GPU. In the context of deep learning, VRAM is important because it allows the GPU to handle large datasets and complex models by providing enough memory to store and manipulate the visual data quickly and efficiently.

💡SATA SSDs

SATA (Serial ATA) SSDs (Solid State Drives) are a type of storage device that uses the SATA interface to connect to a computer's motherboard. They are faster and more reliable than traditional hard drives and are used in the video's recommended setup for their combination of performance and cost-effectiveness.

💡Custom Rig

A custom rig refers to a computer system that is built from individual components chosen by the user to meet specific needs or preferences. In the video, the speaker compares a custom-built machine with fewer cores and less memory to the recommended server setup, highlighting the benefits of the latter in terms of raw power and cost-effectiveness for deep learning tasks.

💡Cloud GPU

Cloud GPUs refer to graphics processing units that are available as a service over the internet, allowing users to rent computing power for deep learning and other tasks without having to own the physical hardware. In the video, the speaker discusses the benefits and drawbacks of using cloud-based GPU solutions compared to owning and managing hardware.

💡Cost Analysis

Cost analysis is the process of evaluating the expenses associated with a particular option or decision, often to determine the most cost-effective or economically viable choice. In the video, the speaker conducts a cost analysis of different deep learning hardware setups, taking into account factors like upfront costs, monthly operating expenses, and performance metrics.

💡Collaboratory

Collaboratory, often referred to as Colab, is a cloud-based platform that provides free access to Jupyter notebooks and GPUs for data analysis and machine learning projects. It allows users to write and execute code in a collaborative environment without the need for expensive local hardware.

Highlights

The speaker shares their opinion on the best deep learning rig for the money in 2024.

The speaker has previously posted videos about working with Dell PowerEdge R720 servers, which they consider cost-effective for deep learning.

The recommended setup includes a 40-core server with 256GB of DDR3 RAM and a RAID controller.

Two 1.2 terabyte SAS hard drives come with the server, which the speaker suggests using as a separate virtual drive for booting.

The speaker pairs the server with two Tesla P4s at $187 each, providing a significant amount of compute power for a low cost.

The total VRAM provided by the Tesla P4s is 48 gigabytes, which the speaker considers ample for most deep learning tasks.

The speaker recommends Teamgroup SSDs or SATA SSDs for their reliability and compatibility.

The total cost for the recommended setup is around $1,000, which the speaker believes offers excellent value for an AI/ML/DL setup.

The monthly operating cost of the setup is approximately $50, based on the speaker's electricity costs and usage.

The speaker compares the recommended setup to a custom rig, highlighting the benefits of more cores and RAM in the recommended setup.

The speaker also discusses the option of using cloud GPUs, but prefers the ease and cost-effectiveness of directly accessing and managing hardware.

The speaker provides a detailed cost analysis of different options, including cloud-based solutions and pay-per-compute services.

The speaker concludes that assembling older hardware yourself remains a valuable strategy for cost-effective deep learning in 2024.

The speaker invites viewers to share their thoughts and questions in the comments section.

The speaker encourages viewers to like, subscribe, and provide feedback to support the creation of more content.