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, recommending a refurbished Dell PowerEdge R720 server with two 20-core CPUs, 256GB DDR3 RAM, and two Tesla P4 GPUs. The setup offers 48GB of VRAM and 10TB of storage for under $1,000, with a monthly operating cost of about $50. The host compares this to custom rigs and cloud GPU options, highlighting the cost-effectiveness and flexibility of the recommended setup for AI and machine learning tasks.

Takeaways

  • 🚀 The speaker recommends a Dell PowerEdge R720 server as the best deep learning rig for the money in 2024.
  • 💡 The server comes with 40 cores (20 cores per CPU), 256GB DDR3 RAM, and a RAID controller.
  • 💻 Two 1.2TB SAS hard drives are included, which can be used as a separate virtual drive for booting.
  • 🔗 The server was purchased from SaveMyServer, and the speaker has had positive experiences with their deals.
  • 💰 Paired with two Tesla P4 GPUs at $187 each, the setup offers 48GB of VRAM for compute power.
  • 🔧 Additional adapters are needed for the GPU installation, which the speaker details in a separate video.
  • 🗂️ Five TeamGroup SATA SSDs (2TB each) provide 10TB of fast storage for data and projects.
  • 💸 The total cost for the entire setup is around $1,000, offering a high performance-to-cost ratio.
  • ⚡ The server's average monthly power consumption is 3.4 kilowatts, resulting in an electricity cost of around $50 per month.
  • 🔄 The speaker compares this setup to a custom rig with fewer cores and RAM, and a cloud-based solution, highlighting the cost-effectiveness of the server.
  • 🌐 Cloud GPU solutions, while convenient, are more expensive and come with limitations like data transfer caps and network usage.
  • 📈 The speaker suggests that for deep learning tasks, the older hardware's performance is still relevant and valuable.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the best deep learning rig for the money in 2024.

  • What type of server does the speaker recommend for deep learning in 2024?

    -The speaker recommends using a refurbished Dell PowerEdge R720 server for deep learning in 2024.

  • How many CPU cores does the recommended server have?

    -The recommended server has a total of 40 CPU cores, with two CPUs at 20 cores each.

  • What is the RAM specification of the server?

    -The server comes with 256 GB of DDR3 RAM, 16 modules at 16 GB each running at 1600 MHz.

  • What kind of hard drives are included with the server?

    -The server includes two 1.2 terabyte SAS hard drives.

  • What GPU does the speaker recommend for this setup?

    -The speaker recommends using two Tesla P4 GPUs, costing $187 each.

  • How much VRAM does the total setup provide?

    -The total setup provides 48 gigabytes of VRAM.

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

    -The estimated monthly electricity cost is around $1.50, based on a power consumption of 3.4 kilowatts and an electricity rate of 12 cents per kilowatt-hour.

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

    -The speaker compares the server setup, which has 40 CPU cores and more RAM, to a custom-built rig with 6 CPU cores and 32 GB of RAM, highlighting that the server setup offers more raw power for the same money.

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

    -The speaker acknowledges that cloud GPU solutions are a good option if money is not a constraint, but prefers the ease of directly accessing hardware and the lower cost of self-managed servers.

  • What is the total upfront cost of the recommended server setup?

    -The total upfront cost of the recommended server setup is around $1,000.

Outlines

00:00

🤖 Best Deep Learning Rig for 2024

The speaker introduces a video discussing the best deep learning rig for the money in 2024. They share their opinion on the most cost-effective setup, which includes Dell PowerEdge R720 servers, two Tesla P4 GPUs, and Teamgroup SSDs. The focus is on performance and cost, with a comparison to other common strategies like custom rigs and cloud GPU services.

05:01

💰 Cost Analysis of the Recommended Setup

The speaker provides a cost analysis of the recommended deep learning setup, including the initial investment and monthly operational costs. They compare the total cost to a custom rig and cloud-based solutions, highlighting the性价比 of their suggested configuration. The speaker also mentions the power consumption and electricity costs associated with running the rig.

10:03

🔍 Comparing Different Deep Learning Approaches

The speaker compares the specifications and performance of their recommended setup with a custom rig and cloud-based solutions. They discuss the trade-offs between CPU cores, RAM, storage, and GPU capabilities. The speaker emphasizes that while the recommended setup uses older hardware, it still provides significant performance for the price.

15:04

📝 Final Thoughts and Alternative Options

The speaker concludes by discussing alternative options such as pay-per-compute services and sharing their personal experience with platforms like Kaggle and Colab. They mention the limitations of these services, such as job timeouts and the complexity of cost analysis for deep learning tasks. The speaker reiterates their belief in the value of older hardware for deep learning and invites viewers to ask questions or provide feedback.

Mindmap

Keywords

💡Deep Learning Rig

A deep learning rig refers to a computer system specifically configured for deep learning tasks, which involves processing large amounts of data to train artificial neural networks. In the video, the creator discusses the best deep learning rig for the money in 2024, emphasizing performance and cost-effectiveness. The rig mentioned includes older but reliable components like Dell PowerEdge R720 servers and Tesla P4 GPUs.

💡Performance for Money

Performance for money is a measure of how well a product or system performs relative to its cost. It's about getting the best value for the investment. In the context of the video, the creator is looking for a deep learning rig that offers the highest performance at the lowest possible price, which is a common consideration for those entering the field or looking for affordable solutions.

💡Dell PowerEdge R720

The Dell PowerEdge R720 is a server model from Dell that is known for its reliability and solid performance, despite being older. It's often used in the context of deep learning due to its ability to handle multiple GPUs and its robustness. In the video, the creator uses this server as the foundation for their recommended deep learning rig.

💡Tesla P4 GPUs

Tesla P4 GPUs are graphics processing units designed by NVIDIA for data center and cloud computing applications. They are optimized for deep learning and machine learning tasks, offering a significant amount of VRAM and computational power. In the video, the creator pairs the server with Tesla P4s to achieve a high level of compute power at a low cost.

💡Storage

Storage in the context of a computer system refers to the physical devices that hold data. For deep learning, having sufficient and fast storage is crucial for handling large datasets. The video mentions the use of Teamgroup SSDs or SATA SSDs for the rig, offering 10 terabytes of storage space.

💡Power Consumption

Power consumption is the amount of electrical energy used by a device or system over time. It's a critical factor in the total cost of ownership, especially for servers and rigs that run continuously. The video provides an estimate of the rig's power consumption and the associated monthly electricity cost.

💡Custom Rig

A custom rig is a computer system that is built to meet specific requirements, often with a focus on performance in a particular area, such as gaming or deep learning. The video compares a custom rig with the recommended setup, highlighting the trade-offs in terms of cost and performance.

💡Cloud GPU

Cloud GPU refers to the use of graphics processing units that are remotely hosted in the cloud, allowing users to access them on-demand for their computing needs. This can be a convenient option but often comes with higher costs and limitations compared to owning and managing hardware.

💡Cost Analysis

Cost analysis is the process of evaluating the expenses associated with a particular investment or project, often to determine its financial viability. In the video, the creator performs a cost analysis to compare different deep learning rig options and to justify the recommended setup's value.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data. The video's discussion of deep learning rigs is directly related to machine learning applications, as these rigs are used to train and run machine learning models.

💡Hardware Management

Hardware management refers to the process of purchasing, assembling, and maintaining computer hardware. The video emphasizes the benefits of self-managing hardware, such as the ability to upgrade and customize the rig as needed, which can be more cost-effective and flexible than cloud-based solutions.

Highlights

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

The speaker recommends using Dell PowerEdge R720 servers for their cost-effectiveness and reliability.

The recommended server configuration includes 40-core CPUs, 256GB DDR3 RAM, and a RAID controller.

The server comes with two 1.2TB SAS hard drives, which the speaker suggests using as a separate virtual drive for redundancy.

The speaker pairs the server with two Tesla P4 GPUs for a total of 48GB VRAM at a low cost.

The total cost for the recommended setup is around $1,000.

The monthly operating cost, including electricity, is estimated to be around $50 cents.

The speaker compares this setup to a custom rig with fewer cores and RAM, highlighting the value of the recommended configuration.

Cloud GPU solutions are mentioned as an alternative, but the speaker prefers direct hardware access for ease and cost-effectiveness.

The speaker has been using Leno (now part of AMD) for over three years and finds it a cheaper alternative to major cloud providers.

The speaker discusses the importance of RAM, storage, and GPU for AI, ML, and DL applications over CPU clock speed.

The upfront cost of the recommended setup is the least compared to custom and cloud-based solutions.

The speaker mentions that the older hardware does not significantly impact performance for deep learning tasks.

The speaker suggests that pay-per-compute or hourly cloud services may be suitable for some budgets, but they can be less flexible and harder to troubleshoot.

The speaker provides a cost analysis example for using Leno's hourly compute service, showing that it can become expensive with frequent use.

The speaker concludes that self-managing older hardware still offers good value for money in 2024.

The speaker invites viewers to comment with questions or feedback and offers to respond.

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