Best GPU for Deep Learning & AI (2020)

Scientists believe that artificial intelligence could lead to a “third industrial revolution,” opening up new possibilities for the future. What is needed now is increased computational power and sophisticated algorithms that will need as much computing power as possible in order to keep developing into an increasingly intelligent species. Which GPU would you recommend buying today?

In 2020, the GPU will continue to be a key piece of technology for deep learning and AI. The current industry standard is Nvidia’s RTX 2080 Ti which costs about $1,200 on Amazon today. However, as new GPUs come out with more powerful AI capabilities in 2019-2020 at higher price points than these cards have been able to offer up until now, it’ll help you decide what you want your next purchase to look like!

The “best gpu for deep learning reddit” is a question that has been asked before. There are many different opinions on what the best GPU for deep learning and AI would be in 2020.

The Best GPU for AI and Deep Learning

For most AI applications, CPUs are too flexible. As a result, GPUs are often utilized. Some manufacturers, on the other hand, are already developing more specialized hardware for machine learning.

• Artificial intelligence only needs a small portion of the instruction set of traditional CPUs.
• It is more efficient and less expensive to use specialized processors for neural networks and other applications.
• GPUs are especially popular because to their ease of use and programmability, although a growing number of companies are producing AI-specific hardware.

NVIDIA GPUs are being used by prominent server makers to enhance their systems for AI and analytical applications. GPUs specialized for AI can conduct hundreds of concurrent computations in a single second, culminating in about 100 trillion floating-point operations per second (TeraFLOPS).

GPUs have been critical in making deep learning cost-effective, but they are not yet ideal for AI needs. As a result, certain manufacturers provide AI chips that have been carefully tuned. With its Deep Learning Unit, Fujitsu has chosen this approach (DLU).

Deep learning models that are state-of-the-art (SOTA) need a lot of memory. Processor SOTA deep learning models demand a lot of VRAM, which isn’t available on regular GPUs. We’ll show you GPUs that can be utilized for Deep Learning and AI models in this test.

(TLDR) Overview of Test Results: Best GPUs for Deep Learning

As of May 2020, these GPUs can train all SOTA language and image models without any issues:

• Nvidia Quadro RTX 8000 (5000-6000$) with 48GB VRAM

• 3500$-4250$ Nvidia Quadro RTX 6000 with 24GB VRAM

• 2200$-2800$ Titan RTX with 24GB VRAM

GPUs capable of training most (but not all) SOTA models include:

• (1000$-1500$) Nvidia Geforce RTX 2080Ti with 11GB VRAM

• 650$-800$ Nvidia Geforce RTX 2080 with 8GB VRAM

• 450$-600$ Nvidia Geforce RTX 2070 with 8GB VRAM

The Nvidia Geforce RTX 2060 Super may still be used to train SOTA models, however the Nvidia Geforce RTX 2060 and lesser variants are no longer appropriate.

Image and Language Models as Benchmarks

1. SOTA Image Models based on Deep Learning

Before meeting the RAM limit, the maximum batch size is:

*The GPU is unable to execute the model owing to a lack of memory.

Benchmarks for performance (number of images processed per second):

*The GPU is unable to execute the model owing to a lack of memory.

2. SOTA Language Models for Deep Learning

Before meeting the RAM limit, the maximum batch size is:

*The GPU is unable to execute the model owing to a lack of memory.

Performance Metrics:

*The GPU is unable to execute the model owing to a lack of memory.

Verdict

1. More VRAM is less beneficial to image models than it is to language models.
2. GPUs with more GPU memory perform better because they can handle bigger batch sizes, allowing the CUDA cores to be fully used.
3. The more VRAM you have, the higher the batch sizes you can process. GPUs with 48GB VRAM can hold 4x the amount of data in batches than GPUs with 11GB VRAM.

Winning GPUs for Deep Learning in Detail

Nvidia Quadro RTX 8000 comes in first place.

GPU with the best performance for Deep Learning models

In the data center, so-called passively cooled GPU accelerators are no exception. The phrase “passive cooling” refers to the fact that the cards themselves do not have fans, but the cooler does have a steady air flow. As a result, the server case or rack guarantees adequate cooling and reduces the risk of hardware failure due to a defective fan. Furthermore, since no fan is required to draw air between the cards, the passively cooled cards may be operated closer together.

Passively cooled graphics cards are yet to be seen in the workstation market, at least not when it comes to high-performance GPUs. The Quadro RTX 8000 Passive and the Quadro RTX 6000 Passive are the two new models from PNY. These, like the active models, employ an NVIDIA GPU with Turing architecture. There are 4,608 shader units, which is the same as the RTX Titan but more than the GeForce RTX 2080 Ti: 72 RT cores and 576 tensor cores. These are the Quadro RTX series’ flagship models. In terms of performance, the two models are almost identical.

The memory expansion is the difference between the Quadro RTX 8000 and the Quadro RTX 6000, whether actively or passively cooled. The Quadro RTX 6000 has 24 GB of GDDR6 with ECC support, while the Quadro RTX 8000 has 48 GB. The memory bandwidth, which is 624 GB/s for each, is likewise the same.

However, passive cooling – to keep with the nomenclature – has an impact on the thermal design power. While the actively cooled cards can handle up to 295 W, the passively cooled cards can only handle 250 W. This results in a 10% power differential between the actively and passively cooled models, favoring the actively cooled ones. As a result, while employing these models, you must make a compromise.

For one card, systems or the cooling of such systems are built for a TDP of 250 W. As a result, this is a value for which the data center segment’s passively cooled GPU accelerators are built. As previously stated, the case must be able to provide enough ventilation and hence cooling. This may be done in any way via the card and does not have to be created in the slot bracket’s direction.

Conclusion: The best GPU for Deep Learning models.

PNY offers the Quadro RTX 8000 Passive and Quadro RTX 6000 Passive to OEMs for such workstations. A Quadro RTX 6000 costs $3,375, while a Quadro RTX 8000 with 48 GB of RAM costs $5,400–in the actively cooled version, of course. It’s unclear if passive models will make it into free trade.

Nvidia Quadro RTX 6000 is ranked second.

GPU with excellent performance for Deep Learning models

The Nvidia Quadro RTX 5000 and Quadro 6000 graphics cards, which specialize in ray tracing, are now available for pre-order. The less expensive RTX 5000 variant, which costs $2,300 including VAT, is already sold out.

The complete configuration is used by the Quadro RTX 6000, which Nvidia has only revealed for workstations thus far. In addition to the complete setup, the model employs twice as many GDDR6 chips per memory controller, for a total of 24 GiBytes. In the interim, at least in the United States, interested parties may pre-order the Turing graphics card. Nvidia is asking approximately $6,300 for this. As an example: 5,000 USD gets you a Quadro P6000 with GP102 complete setup and 24 GB GDDR5X RAM. Professional assistance is, of course, always included in the Quadro rates. Nvidia chooses to offer the full-featured TU104 GPUs as Quadro due to the pricing gap with the Geforce RTX 2080 Ti. Even with the mature 12FFN process, the yield of completely functioning chips may not be the greatest with a chip size of 754 mm2, and workstation users have a (much) greater margin.

At the very least, the Quadro RTX 6000 demonstrates what a future Turing Titan graphics card would look like. Perhaps not with the pricey 24 gigabyte GDDR6, but with fully enabled and perhaps quicker 15 or 16 gigabit per second memory, as soon as it becomes available. But whether or not such a “Titan Xt” will appear is up in the air.

Conclusion: A great GPU for Deep Learning models.

However, the substantially more costly Nvidia Quadro RTX 6000 may still be pre-ordered for $5,600 USD. Nvidia has pushed the TU102 GPU to its limits with this board, which has 4,608 CUDA, 576 Tensor, and 72 RT cores. 24 GB of GDDR6 RAM and 384-bit memory connection are also included. For the time being, this makes it the most affordable graphics card with a Turing processor. The GeForce RTX 2080 Ti, on the other hand, does not depend on the TU102’s full extension. It also has just 11 GB of RAM.

Nvidia Titan RTX is the third option.

At a lower cost, the best performing GPU for Deep Learning models is available.

If you’re a gamer searching for a lightning-fast graphics card, the Titan RTX should be avoided at all costs. Not even as a supporter. Although the graphics card is the fastest on the market for gamers, the benefit does not justify the astronomically expensive price of $2,700. The GeForce RTX 2080 Ti is always the superior option.

Nonetheless, in terms of GPU and memory, the Geforce Titan RTX is a pretty fascinating graphics card. It is now the fastest graphics card on the market, with the TU102 GPU fully expanded, and it is also very energy efficient. But also because the power target’s maximum is reached very quickly. A true Titan should have a lot more power consumption flexibility. The cooler is also merely attractive from a visual standpoint; the cooling mechanism is inadequate in terms of titanium for the price.

The Titan RTX’s (irrational) boast is its 24 GB memory.

However, when it comes to memory, the Titan stands out from all other GeForce models: 24 GB is by far the most RAM available on a normal graphics card. The AMD Radeon VII (test), which already has 16 GB of memory, is 8 GB behind.

However, for gamers, that much RAM is excessive and will most likely stay so throughout the graphics card’s lifespan. However, for professional applications, this might be a significant benefit.

Finally, unlike the Titan Xp, this is also the case with this Titan, and therefore the actual customer, since corresponding rendering and other applications may sometimes run out of VRAM. The “Prosumer” equivalents, Quadro and Radeon Pro, are even considerably more costly.

The Mifcom system has two hot irons.

Conclusion: The best performing GPU for Deep Learning models at a lower cost

Mifcom’s solution, when combined with the Skylake-X-CPU, provides a very fast, extremely cleanly configured, and even optically matched base for this user group, which cannot completely mirror the utilization of two Titan RTX. Finally, neither the casing nor the graphics cards with the regular cooling may be used in dual mode. As a consequence, despite the high noise level, the heat isn’t dispersed quickly enough, and the higher of the two graphics cards’ base clock reduces by around 400 MHz to 1,350 MHz. This translates to a 25 percent reduction in raw power, which might be more or less visible depending on the application.

Apart from that, multi-GPU operation in professional applications typically runs well, particularly because the second GPU does not have to follow the first in terms of clock speed. However, with games, the issue with dual-GPU only truly begins with games, since just a few titles can utilise SLI properly, if at all. In the near future, the editors will write a separate article on this subject.

Nvidia Geforce RTX 2080Ti (fourth)

In the entry-level class, a good GPU for Deep Learning models

What is the performance of the new Geforce GPUs RTX 2080 Ti and RTX 2080 in contrast to the previous generation’s GTX 1080 and GTX 1080 Ti? Today, we’re playing games with Nvidia’s new Turing architecture, at least in the conventional sense. Because of a dearth of relevant games, the highly promoted capabilities of raytracing and Deep Learning Super Sampling (DLSS) can’t yet be tested experimentally.

We assess performance in current games as usual, compare the newcomers to the GTX 1080 Ti and Co, and speculate on the possibilities of the RT and Tensor cores in our review of the GTX 2080 and GTX 2080 Ti.

Unlike the 2016 launch of the initial Pascal graphics cards, the GTX 1080 and GTX 1070, the GTX 2080 will have partner cards (custom designs) available on September 20th in addition to reference cards (Founders Edition).

Asus, MSI, and Zotac have already sent us test samples, and more are on their way to the editorial office. More experiments will be conducted in the coming days and weeks, but for now, the emphasis is on Nvidia’s own products.

The most noticeable feature of the new Founders Edition is the fully redesigned cooling system: the previous blower-style cooling system with a tiny radial fan is now outdated; both new versions have two 90 mm fans and an aluminum radiator with vapor chamber.

Despite its greater power consumption, the RTX 2080 Ti and RTX 2080 are far cooler and quieter than their predecessors. However, there is a drawback to this method: the waste heat is no longer transferred straight out of the case, but rather spread inside it. As a result, effective case ventilation is recommended (although this also applies without RTX card).

The reference cards feature a basic silver and black design, are well-made, and have a pleasant tactile. This is due, in part, to the graphic cards’ unusually high weight of roughly 2.7 lbs. Only the Ti abbreviation of the inscription, which may be seen on the front and on the aluminum backplate on the back, distinguishes the two types externally. During use, the green “Geforce RTX” logo illuminates.

The new Founders Edition takes up two slots in the case and is the same size as its predecessors, measuring 26.7 x 11.4 x 4.0 cm. The slot bracket has three Displayport 1.4, one HDMI 2.0b, and one USB-C port, as well as extra ports for VR headsets. The so-called NVLink connection on the top side of the graphics card is used to link two graphics cards in SLI using a separately available bridge (85 euro).

Only a direct comparison reveals how much optics and cooling have progressed: The stunning finish gives way to a more straightforward and unfussy radiator cover. If you have a tiny case, the blower style idea will most likely be overlooked. However, other partners have previously disclosed this cooling concept, and most of these bespoke designs should have more cooling surface, as well as a bigger radial fan, than Nvidia’s Founders Edition.

The latest Geforce graphics cards with Turing processors have arrived, but the last secret has still to be disclosed. Both the RTX 2080 Ti and the RTX 2080 outperformed every presently available graphics card in the test, dethroning the GTX 1080 Ti and providing smooth gameplay in 4K/UHD with high or extremely high resolution levels. With that, Nvidia just meets expectations, although thanks to raytracing and DLSS, Turing still has some potential slumbering.

Conclusion: For Deep Learning models in the entry-level class, a solid GPU is required.

However, it will most likely take some time for these elements to become entrenched in games. However, Nvidia has been able to secure the creators’ cooperation, with over 20 titles using at least one of the two new capabilities so far. Although the benchmark sequences employed in the test seem to be a little too far out of the ordinary, they do offer us an indication of Turing’s ability to produce more realistic images or create greater performance with dedicated raytracing and AI cores.

However, the ending is still a little depressing now. The RTX 2080 Ti and RTX 2080 are very fast, but also quite costly gamer graphics cards that are finally fast enough for 4K/UHD panels, but also demand a strong hold on the pocketbook.

When it comes to deep learning and artificial intelligence, it’s all about the technology.

Artificial intelligence (AI) is no exception to the rule that whomever wishes to reap must first sow. The battle to develop the brightest AI applications need specialized high-performance hardware. A computer’s processor isn’t everything.

Learning algorithms must process massive volumes of data in real time. They must be able to make sound judgments in uncertain scenarios while while operating in a highly personalized environment. Backend learning technologies for AI applications such as programmatic advertising, autonomous driving, and intelligent infrastructures have already matured to the point where they can be used. What is lacking is the necessary hardware.

The actual use of AI offers huge problems, in part owing to fundamental flaws in today’s hardware designs. The demise of Moore’s Law will exacerbate these problems. Furthermore, crucial technical data of cyber-physical systems has application-specific limits, such as space requirements, weight, energy consumption, and so on.

In the face of new sorts of threats such as Adversarial Learning, just securing cyber security and data integrity in personal application situations is exceedingly tough (malicious learning based on fraudulent data). Traditional system designs are just incapable of meeting the new demands.

Moore’s Law and Dennard’s Law are coming to an end.

Moore’s Law and Dennard’s Law have been regarded the greatest guides to technological advancement in the industry since the advent of integrated circuits for ordinary silicon chips. Moore’s Law predicts that the number of transistors in an integrated circuit will double every two years (assuming the same production costs). The end of the line is reached at the latest when the components of conventional circuits sink down to the so-called monolayer — a single atomic layer that prevents them from falling any farther below it.

Moore’s Law is about to come to an end. Dennard’s Scaling had already failed in 2005, according to a technical assessment published three years ago by Professor Christian Märtin of the Augsburg University of Applied Sciences (“Post-Dennard Scaling and the Final Years of Moore’s Law. See also the eBook “High Performance Computing: Consequences for the Evolution of Multicore Architectures.”

Dennard’s law asserts that when the fundamental component in a circuit becomes smaller, the voltage gets lower, allowing a greater clock frequency to be obtained with the same power consumption. Although transistors continue to shrink for the time being, owing to phenomena such as leakage current and threshold voltage, the error rate and hence the manufacturing costs of CPUs would for the first time not reduce any more.

Hardware acceleration tailored to the workload

According to UC Berkeley researchers in their October 2017 publication, “A Berkeley View of Systems Challenges for AI,” the only option to continue to shorten the power-to-power consumption-to-procurement cost variable in the future is to build workload-specific hardware accelerators. Domain-specific hardware architectures, composable infrastructures, and edge architectures (see also the eBook “Edge Computing”) may all be of assistance. According to the Berkeley experts, additional advancements can only be made today via advancements in computer designs, not through advancements in semiconductor processes.

Domain-specific processors can only do a few things well, but they do them well. As a result, future servers will be “far more diversified” than ever before. The Berkeley researchers highlight Google’s “Tensor Processing Unit” (TPU), an application-specific AI accelerator in ASIC (Application-Specific Integrated Circuits) design, as a “groundbreaking example.” Google’s TPU conducts deep neural network inference 15 to 30 times quicker than CPUs and GPUs, with 30 to 80 times greater performance per watt.

The current second generation TPU has 45 teraflops, is floating point capability (for the first time), and has a bandwidth of 600 GBps per ASIC. The resultant module produces 180 TFLOPS in a parallel design with four TPU chips; 64 of these modules create so-called chip-pods in 256 groups with a total performance of 11.5 PFLOPS.

Google uses ASICs, whereas Microsoft uses FPGAs.

Despite its breakthrough nature, Google’s TPU is totally proprietary and not publicly accessible. As a result, Google’s rivals must rely on alternatives for AI tasks.

Microsoft and Intel, unlike Google, use FPGAs. FPGA-based computing instances are available as an Azure service from Microsoft. Altera, an FPGA provider, received a stunning $16.7 billion from Intel.

FPGAs (Field Programmable Gate Arrays) and ASICs (Application-Specific Integrated Circuits) are based on two conflicting philosophies. ASICs are application-specific integrated circuits (ASICs) that are built to highly particular design standards. They have very cheap unit prices, but they can’t be changed.

FPGA-type integrated circuits, unlike ASICs, may be remotely modified to new workloads even after they’ve been placed in the data center (see “The Programmable Data Center” eBook). According to Dr. Randy Huang, FPGA Architect of Intel’s Programmable Solutions Group, chip development from concept to prototype takes just six months for FPGAs and up to 18 months for ASICS. When compared to GPU acceleration, both ASICs and FPGAs have a great energy efficiency. In floating point computations, GPUs (Graphical Processing Units) prevail with a high maximum performance.

GPUnktet: taking advantage of the situation

GPUs outperform traditional CPUs in AI applications by a wide margin due to their huge parallelizability. According to the manufacturer, compared to a system with just one single-socket CPU, the Nvidia GPU “Tesla” provides up to 27 times the acceleration in the inference phase of neural networks.

The market for AI accelerators in data centers is dominated by Nvidia. Within the “Google Cloud Platform,” even Google employs the “Tesla P100” and “Tesla K80” GPUs.

In recent months, Nvidia’s data center sales have increased dramatically. With astonishing seriousness, the GPU market leader has reacted by aggressively broadening its technology portfolio away from gaming and toward AI… However, he has made no pals.

Nvidia restricts the usage of the more cheap “GeForce GTX” and “Titan” GPUs in data centers, according to the current EULA licensing conditions for graphics card drivers. Only block-chain processing is permitted in data center settings. The reason for this, according to the vendor, is the remarkable heat resistance requirements for operating in high-density systems under the stress of intensive AI workloads.

The limiting element is the driver.

Driver upgrades are no longer required for current data center users of these GPUs. The hardware can only give a fraction of its potential performance without Nvidia’s proprietary drivers, which are regularly updated through bug patches. The warranty for the linked hardware is immediately invalid if the user violates the EULAs.

As a result, the new Nvidia EULAs have a greater impact on data centers. Vendors of AI systems will have to rely on 10x more costly Tesla GPUs, such as the Tesla “V100” CPUs (the lower-performance “Quadro” series is designed for visual applications like industrial design, not neural networks).

The performance gap between a GPU and a CPU is expanding due to Moore’s Law’s physical limits. If current trends continue, the GPU is expected to outperform single-threaded CPU performance by 1,000 times by 2025, according to Nvidia’s own blog.

Even a market leader, though, would be wise to refrain from praising the day before the evening.

IPUs, DPUs, DLUs…

GPUs are by no means the sole solution to fulfill AI applications’ performance needs. Both risk-averse chip titans like Intel and Fujitsu, as well as venture capitalists backing a slew of completely unknown start-ups, are banking on it.

The Intelligence Processing Unit (https://www.graphcore.ai/) is the first chip architecture designed from the bottom up for machine learning workloads, according to the British-Californian Graphcore (https://www.graphcore.ai/). VC firms such as Sequoia and Robert Bosch Venture Capital GmbH have backed the start-up due to its revolutionary processing unit and accompanying graph programming framework “Poplar.”

Wave Computing, a Californian firm, is competing with their “Wave DPU” (Dataflow Processing Unit). By foregoing the CPU/GPU co-processor design, the business has created a solution that aims to alleviate the bottlenecks of traditional system architectures. Wave Computing hopes to showcase the possibilities of the new architecture in the data center with a highly scalable 3U appliance for machine learning.

Cerebras Systems, based in California, produces neural network processors and is currently in stealth mode.

With the “DLU,” Fujitsu has forged its own way (Deep Learning Unit). The DLU’s outstanding performance is due, among other things, to a novel data type dubbed “Deep Learning Integer” and the DPU’s “INT8”,16 accumulator. These allow the CPU to conduct integer calculations within deep neural networks with varied precision of 8 bits, 16 bits, and 32 bits without affecting the overall model’s accuracy.

Conclusion and Prognosis

To avoid cannibalizing their existing profitable processor lines, top chip makers simply placed alternative technologies such as quantum computers and neuromorphic systems on hold as long as Moore’s Law could be followed. The development labs are now a hive of activity.

In the tight fight between the top dogs and the challengers – in hardware – the next generation of AI systems promises to considerably accelerate the fast growth of AI research.

Is there any intelligence outside? Multiple layers of defense

Intel has also picked up on the signals of the times. After Nvidia’s licensing deal ended, AMD provided the CPU giant with GPU technology, albeit just for laptops for the time being. Intel appears to not want to leave anything to chance when it comes to AI applications, and has a number of contenders: Altera FPGAs, Nervana Systems ASICs, a 49-qubit quantum computer called “Tangle Lake,” neuromorphic chips called “Loihi,” and the “Movidius VPU” (Vision Processing Unit) for edge deep learning applications in autonomous IoT terminals.

Intel has bought Nervana Systems, a $408 million SaaS platform company with an AI cloud. This is presently operating on Titan X GPUs from Nividia. The Nervana Engine, an application-specific ASIC processor under development, is projected to eliminate this reliance in the near future. The ASIC is also projected to outperform the Nividia Maxwell GPU by a factor of ten.

Under the code name Loihi, Intel is also working with neuromorphic semiconductors. The technical wonder, which was created using 14-nanometer technology, contains a total of 130,000 neurons and 130 million synapses that have been integrated as circuits.

Nvidia, on the other hand, does not want to put all of its eggs in one basket. A hybrid architecture will be used in the next-generation “DrivePX” platform for autonomous cars. A DLA (Deep Learning Accelerator) in ASIC design is employed in addition to an ARM CPU and a Volta-GPU.

According to Research and Markets, the AI industry is expected to increase at a pace of 57.2 percent every year. At this pace, the market will be worth 58.97 billion dollars by 2025, which means there will be plenty of opportunity for numerous different designs.

The “best budget gpu for deep learning 2020” is a question that has been asked for a while now. The answer is still up in the air and will likely be answered by 2020.

Frequently Asked Questions

Which GPU is good for deep learning?

A: A good GPU for deep learning is usually NVIDIA’s GTX 1060.

Is RTX 3080 good for deep learning?

A: RTX 3080 is a powerful graphics card.

What is a good GPU for TensorFlow?

A: The best GPU for TensorFlow is a GTX 1080 Ti.

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