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 Dell PowerEdge R720 servers, Tesla P4 GPUs, and Teamgroup SSDs. He compares this configuration with custom rigs and cloud GPU options, highlighting the balance of performance and affordability. The host emphasizes the importance of considering total costs, including electricity and potential upgrades, and shares his positive experience with Leno, a cloud service provider offering competitive pricing.

Takeaways

  • 💡 The speaker focuses on discussing the best deep learning setup for the value in 2024.
  • 🚀 They recommend using Dell PowerEdge R720 servers for their reliability and cost-effectiveness.
  • 🔢 The suggested configuration includes 40-core CPUs, 256GB of 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 with RAID 1 for redundancy.
  • 🔧 Pairing the server with two Tesla P4s at $187 each provides significant computing power at a low cost, offering 48GB of VRAM.
  • 🛠️ Additional adapters are needed for installation, which the speaker details in a separate video.
  • 💰 The total cost for the recommended setup is approximately $1,000.
  • 💡 The monthly operating cost is calculated to be around $50 based on an average power consumption of 3.4 kilowatts and an electricity rate of 12 cents per kilowatt-hour.
  • 🔄 The speaker compares this setup to a custom rig and cloud-based solutions, highlighting the benefits of the former in terms of raw power and cost.
  • 🌐 Cloud GPU solutions, while convenient, are more expensive and come with limitations such as data transfer caps and monthly costs.
  • 📈 The speaker suggests that for budget-conscious users, exploring pay-per-compute or hourly solutions like those offered by Leno could be an option, though they have their own drawbacks.

Q & A

  • What is the main topic of the video?

    -The main topic of the video 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 for deep learning.

  • What are the specifications of the recommended server?

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

  • How much RAM does the recommended server come with?

    -The server comes with 256 GB of RAM.

  • What type of GPUs does the speaker pair with the server?

    -The speaker pairs the server with two Tesla P4 GPUs.

  • What is the total cost of the recommended setup?

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

  • How much does it cost to operate the setup monthly?

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

  • What are the advantages of using older hardware for deep learning according to the speaker?

    -The advantages of using older hardware for deep learning include lower cost, the ability to add components and customize as needed, and the fact that performance is still adequate for many tasks despite the age of the hardware.

  • How does the speaker compare cloud-based GPU solutions to the recommended setup?

    -The speaker compares cloud-based GPU solutions by highlighting that while they offer access to newer GPUs, they come with higher costs, less storage, and more restrictions such as data transfer caps. The speaker prefers the ease and cost-effectiveness of directly accessing and managing hardware.

  • What other options does the speaker mention for deep learning setups?

    -The speaker mentions custom rigs, cloud GPU solutions, and pay-per-compute or hourly services from providers like Leno (before being acquired by AMI), Kaggle, and Colab as other options for deep learning setups.

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

    -The speaker's final verdict is that the best deep learning setup for the money is the older hardware setup they recommended, which offers a balance of performance and cost-effectiveness, even when compared to newer custom rigs or cloud-based solutions.

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. They share their opinion on the most cost-effective approach, comparing it to common strategies. The speaker discusses their experience with Dell PowerEdge r720 servers, highlighting their reliability and value for those new to deep learning or needing affordable access to resources for large language models and computer vision. The speaker details their latest build, emphasizing the performance and cost-effectiveness of a 40-core server with 256GB DDR3 RAM, two 1.2TB SAS hard drives, and the addition of two Tesla P4 GPUs for a total of 48GB VRAM. They mention the need for adapters and provide a link to the server purchase from SaveMyServer.

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 rig. They challenge the audience to find a better setup for less money and discuss the monthly operating costs, 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 they built in the past, noting the differences in CPU cores, RAM, and storage. They argue that while the custom rig may be faster and more modern, it lacks the raw power of the current setup for the same price, making it more suitable for entry-level tasks rather than tackling larger deep learning problems.

10:03

🌐 Comparing On-Premises and Cloud GPU Options

The speaker compares the on-premises setup with cloud-based GPU solutions, highlighting the benefits of direct hardware access and cost-effectiveness. They discuss the specs of the RTX 6000 GPUs offered by cloud services, noting that while these GPUs are more performant, the overall cost is significantly higher, with less storage and RAM. The speaker emphasizes the limitations of cloud solutions, such as data transfer caps and network usage restrictions. They mention Leno, a cost-effective alternative to major cloud providers, and share their positive experience with the service. The speaker also considers pay-per-compute options, acknowledging that these might be more suitable for those on a tight budget but expressing a preference for the flexibility and ease of troubleshooting offered by owning and managing the hardware.

15:04

📈 Cost-Effectiveness of Self-Managed Hardware

The speaker concludes by reiterating the cost-effectiveness of self-managed, older hardware for deep learning tasks. They acknowledge that while the hardware may be older, the performance it provides is still significant, even by contemporary standards. The speaker encourages the audience to explore various options and perform their own cost-benefit analysis. They also mention alternative platforms like Collab and Kaggle, but express dissatisfaction with these services due to their limitations and shared compute resources. The speaker provides a real-world example of the cost of using Leno's compute service, illustrating that hourly rates may appear cheap but can add up quickly with extensive usage. They advocate for the value of investing in and assembling hardware, managing it oneself as the best strategy for 2024.

🎉 Wrapping Up and Encouraging Feedback

The speaker wraps up the discussion by inviting questions and comments from the audience, offering to respond and provide feedback. They remind viewers to like and subscribe to support the channel's growth and produce better content. The speaker also suggests buying them a coffee as a form of support, with a link provided in the video description. They conclude by thanking 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 video, the speaker is discussing the best configurations for a computer setup to perform deep learning tasks, such as training large language models or working with computer vision applications. The emphasis is on achieving high performance for the cost invested in the hardware.

💡Performance for the Money

This phrase refers to the cost-effectiveness of a particular hardware setup or solution in terms of the computing performance it provides. The speaker is focused on finding the best deep learning rig that offers the highest performance relative to its cost, aiming to get the most out of the investment.

💡Dell Power Edge R720 Servers

Dell Power Edge R720 Servers are a line of server hardware that the speaker has used in the past for their deep learning projects. These servers, while older, are described as reliable workhorses that still offer a lot of value, especially for those new to deep learning or looking for an affordable entry into the field.

💡RAID Controller

A RAID (Redundant Array of Independent Disks) controller is a component that manages multiple hard drives in a computer system, allowing for data redundancy and improved performance. In the context of the video, the RAID controller is part of the server setup and contributes to the overall reliability and effectiveness of the deep learning rig.

💡Tesla P4s

Tesla P4s are a line of graphics processing units (GPUs) designed by NVIDIA for use in servers and data centers. These GPUs are optimized for deep learning and other high-performance computing tasks. In the video, the speaker pairs two Tesla P4s with the server to provide the necessary computational power for deep learning applications.

💡SSDs (Solid State Drives)

Solid State Drives, or SSDs, are a type of storage device that uses flash memory to store data. They are known for their fast read and write speeds, which make them ideal for use in high-performance computing environments. In the video, the speaker recommends using SSDs for the deep learning rig due to their speed and reliability.

💡Custom Rig

A custom rig refers to a personally assembled computer system that is tailored to specific needs and requirements. In the context of the video, the speaker compares a custom rig with fewer cores and less memory to the recommended server setup, highlighting the benefits of the latter in terms of performance and cost-effectiveness.

💡Cloud GPU

Cloud GPUs refer to graphical processing units that are accessed remotely through cloud computing services. Users can rent these GPUs on a pay-as-you-go basis, which can be a flexible and powerful option for certain applications. However, the speaker discusses the potential downsides, such as higher costs and data transfer limits.

💡Cost Analysis

Cost analysis is the process of evaluating the expenses associated with a particular solution or approach, often to determine its affordability or value for money. In the video, the speaker conducts a cost analysis to compare different deep learning setups and find the one that offers the best performance at the lowest cost.

💡Power Consumption

Power consumption refers to the amount of electrical energy used by a device or system over a period of time. In the context of the video, the speaker discusses the power consumption of the deep learning rig, which is an important factor in the overall operating cost of the setup.

💡Upfront Cost

Upfront cost refers to the initial expense required to start a project or purchase a product. In the video, the speaker compares the upfront costs of different deep learning setups, highlighting that the recommended server setup has the lowest initial investment.

Highlights

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

The speaker has experience working with Dell PowerEdge r720 servers, which they consider reliable and cost-effective.

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

The server comes with two 1.2 terabyte SAS hard drives, which can be used as a separate virtual drive for redundancy.

Pairing the server with two Tesla P4s provides a significant amount of compute power at a low cost.

The total cost of the recommended setup is around $1,000, offering a high-performance deep learning rig at a budget-friendly price.

The monthly operating cost is estimated at around $50, based on an average power consumption of 3.4 kilowatts.

Comparing the recommended setup to a custom rig, the latter is more expensive with fewer cores and less RAM.

The speaker prefers the ease of directly accessing and managing hardware over cloud-based solutions.

Cloud GPU solutions, while convenient, can be more expensive with additional costs for storage, RAM, and data transfer.

The recommended setup offers 40 CPU cores, 256GB RAM, 10TB storage, and two GPUs, providing substantial performance for the price.

The older hardware does not significantly impact performance for deep learning tasks, making it a valuable investment.

The speaker suggests that for those on a budget, there are pay-per-compute or hourly solutions available, though they may be less flexible.

The speaker's personal experience with cloud services like Kaggle and Collab has been underwhelming due to shared compute time and job submission difficulties.

The speaker provides a detailed cost analysis to demonstrate the value of the recommended setup compared to other options.

The speaker concludes that self-managing older hardware is still a viable and cost-effective solution for deep learning in 2024.

The speaker encourages viewers to reach out with questions or comments to further discuss the topic.