Looking for the best tools for deep learning? Dive into this article! Deep learning relies heavily on powerful computing. Sure, GPUs are popular for this, but don’t overlook the vital role of Solid State Drives (SSDs). Here, we break down why SSDs matter so much in deep learning and compare the top SSDs available. This means you’ll not only save time and money but also make your deep learning tasks smoother and faster. After reading, you’ll be well-equipped to make informed hardware choices for optimum deep learning results. If you’re in the process of building a complete deep learning rig, make sure to check out our comprehensive deep learning workstation guide.
Overview of Best SSDs for Deep Learning
Let’s delve into the specifics of the top SSDs suitable for deep learning applications.
Samsung 980 SSD 1TB
The Samsung 980 SSD is an entry-level NVMe SSD with TLC NAND and a DRAMless controller. This combination of TLC and DRAMless drives is already popular in the market from other turn-key solution providers due to their affordability.
The Samsung SSD 980 uses the “Pablo” controller as its foundation. Although this is not a brand-new NVMe controller, it has been used in the Samsung Portable SSD T7 family for over a year. This controller lacks a DRAM interface and has four NAND channels instead of the eight in Samsung’s high-end NVMe drives. The Pablo controller can saturate a PCIe 3.0 x4 host interface without using eight NAND channels because of the high NAND IO speeds allowed by current generations of 3D NAND.
Specifications
- Interface/Form Factor: x4 PCIe 3.0/NVMe/M.2 2280
- Storage Sizes: 1TB
- Sequential Read: 3,500 MB/s
- Sequential Write: 3,000 MB/s
- 4KB Random Read: 500,000 IOPS
- 4KB Random Write: 480,000 IOPS
- Bus Type: PCIe 4.0 x4 NVMe 1.4
- Controller: Samsung Pablo
- NAND Type: Samsung 128L 3D TLC
- Power Consumption: 5.3 W Max
- TBW: 600 TBW
- Warranty: 5 years
Pros
- Competitive performance
- Large, fast-recovering dynamic SLC cache
- Attractive design
- AES 256-bit hardware encryption
- Software package
- 980 Pro-like endurance and 5-year warranty
Cons
- Slow write speeds after the SLC cache fills
Crucial MX500 2TB 3D NAND
The MX500 has served as Crucial’s premier SATA option for several years. The drive uses a Silicon Motion 2258 controller and a sizable DRAM cache. It is combined with Micron’s 64 Layer TLC NAND, with the 2TB storage provided by a significantly larger 512Gbit die.
The MX500 offers several key features to enhance its performance. The most significant is dynamic write acceleration, which uses an SLC buffer like many drives but can grow or decrease depending on drive status rather than having a fixed size.
Specifications
- Capacity: 2TB
- Interface: 6 Gbps SATA
- Form Factor: 2.5″ and M.2 2280
- Controller: Silicon Motion SM2258
- NAND: Micron 256Gb 64-layer 3D TLC
- Encryption: AES 256-bit
- Sequential Read: 560 MB/s
- Sequential Write: 510 MB/s
- 4KB Random Read: 95,000 IOPS
- 4KB Random Write: 90,000 IOPS
- Endurance: 360 TBW
- Warranty: 5 years
Pros
- Very affordable
- Competitive performance
- Available in 2.5-inch or M.2 SATA versions
- Reliable
Cons
- Endurance rating low for usage cases involving heavy data writes
SK Hynix Gold P31
The Gold P31 SSD from SK Hynix claims to be the first retail SSD to use 128-Layer flash, giving it a competitive advantage. The Gold P31 is an excellent value that will cause you to reconsider spending the additional $25 to $50 on the Samsung 970 EVO Plus.
If you want to upgrade the storage on your laptop, the Gold P31 from SK Hynix is fantastic because it increases both capacity and battery life. Benchmarking tests may show that certain drives outperform the Gold P31, but the SK Hynix is far more power-efficient, allowing longer off-the-charger periods.
Specifications
- Capacity: 1000GB
- Form Factor: M.2 2280 S3
- Interface / Protocol: PCIe 3.0 x4 / NVMe 1.3
- Controller: SK hynix Cepheus
- DRAM: LPDDR4
- Memory: SK hynix 128L TLC
- Sequential Read: 3,500 MBps
- Sequential Write: 3,200 MBps
- Random Read: 570,000 IOPS
- Random Write: 600,000 IOPS
- Security: AES 256-bit encryption
- Endurance (TBW): 750 TB
Pros
- Class-leading power efficiency
- Top-tier performance
- Competitive endurance & 5-year warranty
- Single sided-form factor
- Low cost
- AES 256-bit encryption
Cons
- The 2TB model is a little slower than the 1TB model
- Black PCB only for 2TB capacity
Sabrent Rocket Q4 NVMe PCIe 4.0 2TB
Sabrent’s Rocket Q4 bridges the gap between the Rocket Q and Rocket NVMe 4.0 by providing potential customers with speed, capacity, and affordability. The Rocket Q4 uses Phison’s PS5016-E16 to control up to 4TB of Micron’s 96-Layer QLC flash.
Specifications
- Capacity: 2000GB
- Form Factor: M.2 2280
- Interface / Protocol: PCIe 4.0 x4 / NVMe 1.3
- Controller: Phison E16
- DRAM: DDR4
- Memory: Micron 96L QLC
- Sequential Read: 4,800 MBps
- Sequential Write: 3,600 MBps
- Random Read: 350,000 IOPS
- Random Write: 700,000 IOPS
- Endurance (TBW): 400 TB
Pros
- Capacity up to 4TB
- High performance and efficiency
- Up to 5-years warranty
Cons
- 1-year warranty without registration
- Slow write speed after write cache fills
- Low endurance-per-GB compared to TLC
Samsung 970 EVO Plus 2TB
The Samsung 970 EVO Plus SSD is the most recent model in Samsung’s highly regarded and best-selling range of SSD storage. It offers an NVMe M/N interface, which is ideal for content creators, professional gamers, and tech enthusiasts.
Specifications
- Form factor: M.2 2280
- Capacity: From 250GB to 2TB
- Interface: PCIe 3.0 x4
- Reading speed: 3,500 MB / s
- Write speed: 3,300 MB / s
- Random Read Speed: 620,000 IOPS
- Random Write Speed: 560,000 IOPS
- Particles (NAND Flash): 92L TLC V-NAND
- NAND Flash Controller: Samsung Phoenix
- NAND Flash Type: Samsung V-NAND (TLC)
- Encryption: 256-Bit AES (Hardware-Based)
- Durability: From 150 TBW (250 GB) to 1200 TBW (2 TB)
Pros
- Solid overall performance
- Black PCB
- Excellent software package
Cons
- Could use further efficiency optimization
Crucial P5
The Crucial P5 uses Micron’s six-core NVMe controller and some of the most recent 96L TLC flash. The P5 offers an average endurance rating, a standard 5-year guarantee, and some value-added software as a bonus.
Specifications
- Form Factor: M.2 2280 SSD
- Interface: PCIe Gen 3.0 x4, NVMe
- Capacity: 2 TB
- NAND Type: 3 Bit TLC
- Sequential Read: 3.400 MB/s
- Sequential Write: 3.000 MB/s
- Reliability (MTTF): 1,8 million hours
- TBW: 1.200 TBW
- Warranty: 5 years limited
Pros
- Competitive pricing
- Hardware-based AES 256-bit encryption
- Blacked-out aesthetics
- 5-year warranty
- Software suite
Cons
- Runs hot
- Not quite as performant as SK Hynix or Samsung
WD Red SN700
The WD Red SN700 is a NAS drive with a 4TB capacity. It has sequential read/write rates of up to 3.4/3.1 GBps and random read/write speeds of up to 550,000/520,000 IOPS.
Specifications
- Capacity: 2000GB
- Form Factor: M.2 2280
- Interface / Protocol: PCIe 3.0 x4 / NVMe 1.3
- Controller: WD NVMe Architecture
- DRAM: DDR4
- Memory: Kioxia BiCS4 96L TLC
- Sequential Read: 3,400 MBps
- Sequential Write: 2,900 MBps
- Random Read: 480,000 IOPS
- Random Write: 540,000 IOPS
- Endurance (TBW): 2,500 TB
Pros
- High capacity options
- TLC-based
- Pricing
- Strong sustained performance
- Efficient
Cons
- Middling general performance
- Unexciting, old technology
- No power loss protection
Samsung 870 EVO
The Samsung 870 EVO uses its most recent 6th-gen 128-Layer V-NAND TLC flash and upgraded MKX ‘Metis’ SATA 6Gbps controller. According to Samsung, the 870 EVO has up to 38% more performance than its predecessor for common computing activities, and the 250GB variant has up to 30% more sustained write performance.
Specifications
- Form Factor: M.2 2280 SSD
- Interface: PCIe Gen 3.0 x4, NVMe
- Capacity: 2 TB
- Dimensions: M.2 2280 Standard
- NAND Type: 3 Bit TLC
- Sequential Read: 3.400 MB/s
- Sequential Write: 3.000 MB/s
- Reliability (MTTF): 1,8 million hours
- TBW: 600 TBW
- Warranty: 5 years
Pros
- Reliable and responsive architecture
- Appealing aesthetics
- AES 256-bit encryption
- Capacities up to 4TB
- 5-year warranty
- Software suite
Cons
- Premium price
Comparison Table
| Model | Specifications | Pros | Cons |
|---|---|---|---|
| Samsung 980 SSD 1TB | – x4 PCIe 3.0/NVMe/M.2 2280 – Storage: 1TB – Read: 3,500 MB/s – Write: 3,000 MB/s – 4KB Random Read: 500,000 IOPS – Warranty: 5 years | – Competitive performance – Attractive design – AES 256-bit hardware encryption – Large, fast-recovering dynamic SLC cache – 980 Pro-like endurance and 5-year warranty | – Slow write speeds after SLC cache fills |
| Crucial MX500 2TB 3D NAND | – Capacity: 2TB<br>- Interface: 6 Gbps SATA<br>- Sequential Read: 560 MB/s<br>- Warranty: 5 years | – Very affordable – Competitive performance – Available in 2.5-inch or M.2 SATA versions – Reliable | – Endurance rating low for heavy data writes |
| SK Hynix Gold P31 | – Capacity: 1000GB – Interface: PCIe 3.0 x4 / NVMe 1.3 – Sequential Read: 3,500 MBps – Endurance (TBW): 750 TB | – Class-leading power efficiency – Top-tier performance – Competitive endurance & 5-year warranty – Single sided-form factor – Low cost<br>- AES 256-bit encryption | – 2TB model is slower than 1TB – Black PCB only for 2TB capacity |
| Sabrent Rocket Q4 NVMe PCIe 4.0 2TB | – Capacity: 2000GB – Interface: PCIe 4.0 x4 / NVMe 1.3 – Sequential Read: 4,800 MBps – Endurance (TBW): 400 TB | – Capacity up to 4TB<br>- High performance and efficiency<br>- Up to 5-years warranty | – 1-year warranty without registration – Slow write speed after cache fills – Low endurance-per-GB compared to TLC |
| Samsung 970 EVO Plus 2TB | – Form factor: M.2 2280 – Capacity: 2TB – Interface: PCIe 3.0 x4 – Reading speed: 3,500 MB / s | – Solid overall performance – Black PCB – Excellent software package | – Could use further efficiency optimization |
| Crucial P5 | – Form Factor: M.2 2280 SSD – Interface: PCIe Gen 3.0 x4, NVMe – Capacity: 2 TB – Sequential Read: 3.400 MB/s – Warranty: 5 years limited | – Competitive pricing – Hardware-based AES 256-bit encryption – Blacked-out aesthetics – 5-year warranty – Software suite | – Runs hot – Not quite as performant as SK Hynix or Samsung |
| WD Red SN700 | – Capacity: 2000GB – Interface: PCIe 3.0 x4 / NVMe 1.3 – Sequential Read: 3,400 MBps – Endurance (TBW): 2,500 TB | – High capacity options – TLC-based – Pricing – Strong sustained performance – Efficient | – Middling general performance<br>- Unexciting, old technology<br>- No power loss protection |
| Samsung 870 EVO | – Form Factor: M.2 2280 SSD – Interface: PCIe Gen 3.0 x4, NVMe – Capacity: 2 TB – Sequential Read: 3.400 MB/s – Warranty: 5 years | – Reliable and responsive architecture – Appealing aesthetics – AES 256-bit encryption – Capacities up to 4TB – 5-year warranty – Software suite | – Premium price |
Machine Learning Memory Requirements
Building a machine learning workstation can be a complex task. Understanding the memory requirements is a critical part of the process. While the average memory requirement is 16GB of RAM, some applications may require more. A massive GPU is typically a “must-have”, but considering the memory requirements is essential as it can significantly impact your application performance.
Buying Guide for Best SSD for Machine Learning
When it comes to purchasing an SSD, several factors should be taken into account:
- Read/write speed: An SSD with sequential read or write rates of up to 3500MB/s offers improved performance.
- Storage: An SSD with a capacity of 2 TB is ideal for storing all of your files.
- Security: An SSD with AES 256-bit encryption provides the highest level of data protection.
- Warranty: An SSD with a maximum warranty of five years is ideal to cover any damage and repair costs.
- Heat dissipation: An SSD with thermal protectors can automatically maintain ideal temperatures, thereby reducing performance hit caused by overheating.
- Cloning data: An SSD with Magician software can easily recover lost data and perform transfers.
SSDs: The Unsung Heroes of Deep Learning
Deep learning algorithms involve processing substantial volumes of data. While the hard drive may not inherently bottleneck the deep learning process, inefficient data handling can lead to performance lags. For instance, if data is fetched from the disk only when required (blocking wait), a hard drive with a speed of 100 MB/s can take approximately 185 milliseconds to process an ImageNet mini-batch of size 32.
However, if you fetch data asynchronously before it is used (like torch vision loaders), the mini-batch can be loaded in the same amount of time, while the computation time for most deep neural networks on ImageNet is about 200 milliseconds. This implies that the next mini-batch can be loaded while the current one is still processing, thereby avoiding any performance penalty.
Therefore, for comfort and productivity, an SSD is recommended. Programs start and respond more quickly on SSDs, and they pre-process large files much faster. An NVMe SSD can further enhance your experience compared to a regular SSD.
SSDs in AI and Machine Learning Landscape
AI and Machine Learning (ML) applications have proliferated over recent years, regardless of whether you operate a large corporation with a multimillion-dollar data center or a small business with a few application servers in a server closet. The performance of NVMe SSDs has proven superior for these workloads.
Data center leaders have become more astute in handling these highly transactional workloads in today’s hybrid environment. They leverage high-performance object storage infrastructures running on NVMe SSDs to quickly capture, store, and analyze more data for users.
SSD vs. HDD: A Comparative Analysis
When it comes to AI projects, managing data flow is crucial. The complexity of machine learning memory requirements can be simplified while discussing the use of SSD or HDD for machine learning. A combination of both is typically recommended.
If data is read from the drive as needed (blocking wait), a 100 MB/s hard drive will take about 185 milliseconds for an ImageNet 32 mini-batch. However, if you fetch data asynchronously, the mini-batch can be loaded in 185 milliseconds, while the computation time for most deep neural networks on ImageNet is about 200 milliseconds. This means you won’t face any performance penalty as you load the next mini-batch while the current one is still processing.
For temporary data storage, an SSD is extremely handy to quickly move data as needed. However, for data that won’t be moved frequently or will eventually land in a permanent storage situation, HDD is a cheaper and suitable option.
If you’re not using large datasets and plan on using simulations to train your AI program, you could bypass the need for permanent storage like an HDD to save costs. In such a scenario, SSDs are recommended for comfort and higher productivity: Programs start and respond faster and pre-process large files a little faster. If you purchase an NVMe SSD, you will get an even smoother experience than a regular SSD.
FAQs
How Much Is RAM Needed For Deep Learning?
A general rule of thumb for RAM for deep learning is to have at least as much RAM as you have GPU memory and then add about 25% for growth.
How much SSD do I need for deep learning?
You should be looking for a RAM range of 8GB to 16GB, more preferably 16 GM of RAM. Try to purchase an SSD of size 256 GB to 512 GB for installing the operating system and storing some crucial projects, and an HDD space of 1TB to 2TB for storing deep learning projects and their datasets.
How much VRAM is needed for deep learning?
Your system should meet or exceed the following requirements before you start working with Deep Learning: Dedicated NVIDIA GPU graphics card with CUDA Compute Capability 3.5 or higher and at least 6 GB of VRAM.
Conclusion
This guide provides an in-depth understanding of the role of SSDs in deep learning and offers a comparative analysis of the best SSDs in the market. As the field of AI continues to evolve, staying updated with the latest technological advancements is crucial. Keep exploring for more informative articles on the intersection of technology and AI.
