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What is a GPU?
Graphic Cards are critical components that top the specs sheet lineage of modern-day computing systems. They are quintessential building blocks that offer the computational force to render or process high-quality images, process, and enriched graphics that most businesses yearn. Most business systems or IT engines sputter and break down without these essential components. These tiny semiconductor chips play a major role in envisioning next-level graphics, playing high-resolution videos, enabling augmented reality, and creating serendipitous visual experiences.
That’s all to it, right? Hell No…
The barrage of intensifying benefits doesn’t just end here. Graphic Cards have a bigger role to play. Their purpose just begins with rendering images or processing videos and supporting development. They can also take up herculean processing and computing functions as well, thereby transiently reducing the load on the Central Processing Unit (CPU). In terms of sheer computational prowess, GPUs surpass the CPUs in-terms of response speed, and accuracy by attending to repetitive tasks with laser-sharp focus.
Now imagine if the capabilities of these tiny graphic cards are force-multiplied by several folds till they begin to resemble a brick (albeit much lightweight), so that they can crunch through datasets of several gigabytes in mere seconds!
GPUs are real-world manifestation of this amplification. It is an innovation that has broken the status-quo of modern computing – production (process automation and product fault detection), research (visualizing molecular structure/ fluid mechanics/ aerodynamics), cybersecurity (anomaly detection, fraud prevention), and even defense (autonomous weapon design and programming).
How GPUs Work?
Over the years, GPUs have moved away from their original function of graphics rendering and evolved into full-fledged programmable components that are capable of handling diversified assignments.
In simplest terms, CPUs are excellent options for generalised functions which they perform in a systematic sequential manner. However, GPUs are more efficient for tasks which can be fragmented into smaller sub-tasks and processed in parallel by their numerous cores. This fragmentation and parallel processing allow a GPU to accomplish certain specialised functions in few minutes. These would otherwise have taken several CPU days or even months. Nvidia’s A100 GPU can outperform the most advanced CPU by 237 and 30 times respectively in data center inference and image recognition tests.
These path-breaking functionalities make GPUs well-suited for accelerating application speed in certain industries, particularly those domains where AI/ML are involved. Nvidia, one of the largest GPU-manufacturers in the world, hosts an entire library of applications where GPUs can be deployed.
Intersect360 Research, a leading research analyst firm, discovered that 15 of the top 15, and 20 of the top 25 High-Performance Computing applications (HPC) in the world were powered by GPUs as far back as 2017. These applications spanned various domains such as chemical research, fluid dynamics, structural analysis, environmental modeling, geophysics, visualization/ image processing, and physics.
GPU Use Cases
Over the years, the superb processing speeds delivered by GPUs have been utilised across different sectors and businesses. They deliver capabilities that range from large-scale (production automation) to niche scholarly uses (ancient text digitisation). Key sectors & use cases include:
- Artificial Intelligence (AI) – Payment fraud detection, Internet of Things (IoT), Object detection and dense captioning of videos, Optical Character Recognition/ Handwritten Character Recognition (OCR/HCR) for digitisation of land records, out-of-print books, medical records, conversational AI and customer support chatbots, etc.
- Machine Learning (ML) – Customer analytics, Predictive analytics, personalized recommendations for eCommerce, Natural Language Processing (NLP) and speech recognition for speech-to-text transcription.
- Game development – 3D modelling, textures and rendering.
- Manufacturing automation – visual rendering, process automation and management, IoT for fault detection and logkeeping, smart systems for industrial inspections, AutoCAD for architecture, etc.
- Education & research – satellite land mapping, fluid mechanics, aerodynamics, biotechnology, protein molecular visualization, and so on.
- High-Performance Computing (HPC) – theoretical physics, space exploration, quantum computing, stock market algo trading.
- Big data & Data analytics – weather forecasting and climate modelling, eCommerce demographic targeting, political profiling, etc.
- Cybersecurity – Identity & Access Management (IAM), fraud detection & prevention, biometric tracking.
- Healthcare – medical imaging and diagnostics, bioinformatics, drug design, molecular analysis, etc.
- Cryptocurrency development research and mining.
- Warehousing & logistics – workforce & shipment tracking, fleet mobilization, supply chain management, automated inventory management.
- Traffic/ crowd management – automatic license plate detection and recognition, people counter, satellite-assisted traffic control and diversion.
Are GPUs a Strategic Investment for Your Business?
At your individual/ organization/ institution level, would you say you are:
- Developing AI/ML tools or training ML models that can be licensed to other organizations/ industries
- Dependent on highly dynamic visualization for manufacturing or biotechnological innovations
- Creating detailed CAD documents for architecture projects
- Investing in the stock market via algorithmic trading
- Churning out exceptional artworks or immersive video content
- providing predictive analytics consulting whether in the fields of business development/ business intelligence (BD/ BI), consumer behaviour or even agriculture?
- Researching cryptocurrencies or developing economic prediction models
- Constructing a foolproof system to detect and prevent financial fraud, pilferage and malfeasance?
- Researching fluid mechanics, climate patterns, traffic movements, subatomic models, quantum physics, etc.?
If the answer is an affirmative yes, you definitely need a GPU to accelerate your undertaking by manifolds.
A lot can be achieved if certain workloads can be accomplished in mere days instead of several months. If CAD/ video editor tools didn’t take hours to load and buffer content before editing could even begin. Or if rendering floor-to-ceiling size visuals could be a breeze!
How Much will a GPU Cost?
Benefits Surely Outweigh the Cost. Nvidia A100-80GB GPU is available for approximately USD 14,000. On eBay it is listed for USD 16,299 (+shipping). Dell is offering the same for USD 23,667. The AMD EPYC 77xx server processor series is available for USD 7,000 approx.
GPU prices vary from vendor to vendor and geographical location to location. Chipmakers like Nvidia and AMD supply vendors with Manufacturer’s Suggested Retail Price (MSRP) along with advice regarding pricing strategies, but from thereon it varies. Given their tremendous capabilities and nanometer-scale semiconductor architecture, GPUs are exorbitant components from the get-go and their prices further fluctuate wildly depending on market conditions. For example, in recent years, cryptocurrency mining has precipitated GPU shortage and catapulted prices exponentially, though this is expected to ease gradually now that Ethereum has shifted to less electricity-guzzling proof-of-stake blockchain.
Nonetheless, it is not always feasible for organisations to invest several thousand dollars in a single go on setting up a data center even when the financial and technological returns are highly lucrative. Furthermore, Moore’s law has been indifferently consigned to history as the technology advances in a whirlwind manner – the necessity to continuously invest to keep up-to-date with technological developments is no longer farfetched.
Then there is the question of technical know-how regarding the surrounding systems that will support the incorporation of a GPU in an individual’s/ organization’s workflow. The services of a qualified IT server specialist cannot be overstated when equipment worth thousands of dollars is to be handled. This entails additional costs, and we haven’t even begun delving into electricity and maintenance costs! Is the investment justified?
What Next? The Case for Cloud GPUs
The ideal recourse in such situation is to enlist the services of Cloud GPU providers, companies which purchase and maintain GPUs and offer them on a pay-as-you-go basis via cloud. This generates several self-evident advantages:
- Individuals/ organizations which use GPUs need not undertake recurrent investments in technology procurement, setup and maintenance.
- Organisations can now utilise operating expenditure (rather than CapEx) to finance their day-to-day GPU-powered operations/ research – a win-win scenario from taxation perspective.
- IT teams (often already overstretched) are liberated and can now focus on R&D crucial to the organization’s continued viability in cut-throat industries.
- Lastly, a pay-as-you-go model allows a server instance to be turned off when not being used – the GPU equipment does not sit in a corner gathering dust (and evoking grief at financial wastefulness) when the project/ purpose is completed.
Ace Cloud Hosting is offering Nvidia A100s, one of the fastest data center GPUs in the world, starting at just USD 2400/mo. Now, you can leverage the innovative OpenStack technology as the underlying backbone to deliver a highly scalable and efficient architecture as required for varied AI/ML/HPC or research purposes.
Intrigued to know how a GPU can accelerate your workload, reach out to our consultant at +1-855-980-2150 (US) or +91-981-110-4802 (India).
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