#high performance cloud computing

[buysellram]

Efficient GPU Management for AI Startups: Exploring the Best Strategies

The rise of AI-driven innovation has made GPUs essential for startups and small businesses. However, efficiently managing GPU resources remains a challenge, particularly with limited budgets, fluctuating workloads, and the need for cutting-edge hardware for R&D and deployment.

Understanding the GPU Challenge for Startups

AI workloads—especially large-scale training and inference—require high-performance GPUs like NVIDIA A100 and H100. While these GPUs deliver exceptional computing power, they also present unique challenges:

High Costs – Premium GPUs are expensive, whether rented via the cloud or purchased outright.

Availability Issues – In-demand GPUs may be limited on cloud platforms, delaying time-sensitive projects.

Dynamic Needs – Startups often experience fluctuating GPU demands, from intensive R&D phases to stable inference workloads.

To optimize costs, performance, and flexibility, startups must carefully evaluate their options. This article explores key GPU management strategies, including cloud services, physical ownership, rentals, and hybrid infrastructures—highlighting their pros, cons, and best use cases.

1. Cloud GPU Services

Cloud GPU services from AWS, Google Cloud, and Azure offer on-demand access to GPUs with flexible pricing models such as pay-as-you-go and reserved instances.

✅ Pros:

✔ Scalability – Easily scale resources up or down based on demand. ✔ No Upfront Costs – Avoid capital expenditures and pay only for usage. ✔ Access to Advanced GPUs – Frequent updates include the latest models like NVIDIA A100 and H100. ✔ Managed Infrastructure – No need for maintenance, cooling, or power management. ✔ Global Reach – Deploy workloads in multiple regions with ease.

❌ Cons:

✖ High Long-Term Costs – Usage-based billing can become expensive for continuous workloads. ✖ Availability Constraints – Popular GPUs may be out of stock during peak demand. ✖ Data Transfer Costs – Moving large datasets in and out of the cloud can be costly. ✖ Vendor Lock-in – Dependency on a single provider limits flexibility.

🔹 Best Use Cases:

Early-stage startups with fluctuating GPU needs.

Short-term R&D projects and proof-of-concept testing.

Workloads requiring rapid scaling or multi-region deployment.

2. Owning Physical GPU Servers

Owning physical GPU servers means purchasing GPUs and supporting hardware, either on-premises or collocated in a data center.

✅ Pros:

✔ Lower Long-Term Costs – Once purchased, ongoing costs are limited to power, maintenance, and hosting fees. ✔ Full Control – Customize hardware configurations and ensure access to specific GPUs. ✔ Resale Value – GPUs retain significant resale value (Sell GPUs), allowing you to recover investment costs when upgrading. ✔ Purchasing Flexibility – Buy GPUs at competitive prices, including through refurbished hardware vendors. ✔ Predictable Expenses – Fixed hardware costs eliminate unpredictable cloud billing. ✔ Guaranteed Availability – Avoid cloud shortages and ensure access to required GPUs.

❌ Cons:

✖ High Upfront Costs – Buying high-performance GPUs like NVIDIA A100 or H100 requires a significant investment. ✖ Complex Maintenance – Managing hardware failures and upgrades requires technical expertise. ✖ Limited Scalability – Expanding capacity requires additional hardware purchases.

🔹 Best Use Cases:

Startups with stable, predictable workloads that need dedicated resources.

Companies conducting large-scale AI training or handling sensitive data.

Organizations seeking long-term cost savings and reduced dependency on cloud providers.

3. Renting Physical GPU Servers

Renting physical GPU servers provides access to high-performance hardware without the need for direct ownership. These servers are often hosted in data centers and offered by third-party providers.

✅ Pros:

✔ Lower Upfront Costs – Avoid large capital investments and opt for periodic rental fees. ✔ Bare-Metal Performance – Gain full access to physical GPUs without virtualization overhead. ✔ Flexibility – Upgrade or switch GPU models more easily compared to ownership. ✔ No Depreciation Risks – Avoid concerns over GPU obsolescence.

❌ Cons:

✖ Rental Premiums – Long-term rental fees can exceed the cost of purchasing hardware. ✖ Operational Complexity – Requires coordination with data center providers for management. ✖ Availability Constraints – Supply shortages may affect access to cutting-edge GPUs.

🔹 Best Use Cases:

Mid-stage startups needing temporary GPU access for specific projects.

Companies transitioning away from cloud dependency but not ready for full ownership.

Organizations with fluctuating GPU workloads looking for cost-effective solutions.

4. Hybrid Infrastructure

Hybrid infrastructure combines owned or rented GPUs with cloud GPU services, ensuring cost efficiency, scalability, and reliable performance.

What is a Hybrid GPU Infrastructure?

A hybrid model integrates: 1️⃣ Owned or Rented GPUs – Dedicated resources for R&D and long-term workloads. 2️⃣ Cloud GPU Services – Scalable, on-demand resources for overflow, production, and deployment.

How Hybrid Infrastructure Benefits Startups

✅ Ensures Control in R&D – Dedicated hardware guarantees access to required GPUs. ✅ Leverages Cloud for Production – Use cloud resources for global scaling and short-term spikes. ✅ Optimizes Costs – Aligns workloads with the most cost-effective resource. ✅ Reduces Risk – Minimizes reliance on a single provider, preventing vendor lock-in.

Expanded Hybrid Workflow for AI Startups

1️⃣ R&D Stage: Use physical GPUs for experimentation and colocate them in data centers. 2️⃣ Model Stabilization: Transition workloads to the cloud for flexible testing. 3️⃣ Deployment & Production: Reserve cloud instances for stable inference and global scaling. 4️⃣ Overflow Management: Use a hybrid approach to scale workloads efficiently.

Conclusion

Efficient GPU resource management is crucial for AI startups balancing innovation with cost efficiency.

Cloud GPUs offer flexibility but become expensive for long-term use.

Owning GPUs provides control and cost savings but requires infrastructure management.

Renting GPUs is a middle-ground solution, offering flexibility without ownership risks.

Hybrid infrastructure combines the best of both, enabling startups to scale cost-effectively.

Platforms like BuySellRam.com help startups optimize their hardware investments by providing cost-effective solutions for buying and selling GPUs, ensuring they stay competitive in the evolving AI landscape.

The original article is here: How to manage GPU resource?

[casualhydraheretic]

AMD’s MI300X Deal Signals Bold Shift in AI Compute Leadership

AMD’s $400 million agreement to supply 13,000 MI300X GPUs to Crusoe marks a strategic push into the AI compute space, challenging NVIDIA’s dominance with energy-efficient, high-performance alternatives tailored for next-gen workloads.

Read more here - https://dcpulse.com/article/amd-deal-quietly-redefining-leadership-in-ai-compute

[acquaintsofttech]

Expectations vs. Reality—Scalability vs. Elasticity in Cloud Computing

The concept of scalability vs. elasticity in cloud computing often gets mixed, but the reality hits different! In software product engineering service, both scalability and elasticity are needed.

In any system design, both scalability and elasticity hold a distinct significance. It helps businesses to keep their system intact with the latest features, capabilities, and resources. However, deciding on the right steps is still a basic software product development need. We'll eventually talk about the comparison between scalability vs. elasticity in cloud computing in detail in this blog.

What is Cloud Computing in a System?

Its foundation relies on on-demand delivery of servers, data storage, and systems over the internet for users to access them without owning them. underneath the infrastructure.

Cloud computing is the talk of the time, and capability and elasticity serve as top database solutions for handling high traffic. By knowing the static differences between the system scalability and system elasticity, you can make smarter business decisions about its utilization. 

Let's get a quick introduction before diving further into scalability vs. elasticity as a software product engineering service—

CTA 1:

Make your Legacy system more scalable!

Get top database solutions for handling high traffic by Acquaint Softtech! You can consult with the best experts and get relevant business insights to help with your software product development project every now and then!

Let us know!

System Scalability: An Overview

Scalability within a system refers to a system's core capability to handle increased workloads by optimal resource utilization and without compromising its functional perspectives. 

System scalability is more of a long-term strategy; taking the right steps ensures steady business growth. It's one of the two buzzwords in the world of cloud computing, the second being elasticity. People usually expect more from either of these two to be working similarly, but it is to understand for the businesses that these are two different things to get done!

System Elasticity: A Review  

In contrast to system scalability, elasticity is something on a different level. Its foundational concept relies on the system's ability to dynamically adjust and manage its resource allocations based on workload fluctuations.

However, system elasticity is a short-term solution for effortless system management. In comparison to system scalability, elasticity serves to very different purposes. By implementing the right elasticity and network optimization strategies for scalable applications, businesses can explore cost-effective system elasticity and adaptability in modern IT infrastructure.

Scalability Vs. Elasticity In Cloud Computing: Expectations & Reality

The expectations and reality differences of scalability and elasticity within a software product engineering service hold their own practical implementations. Here's a detailed breakdown of anticipating and leveraging these two features within a system—

#1: Expectation: “Scalability and Elasticity Are Similar”

Reality: 

It's a half-truth that system scalability and elasticity are safe. The reality is that they are closely identical in terms of business implementations. So, being closely related doesn't mean they're identical on every stage and level of the work process.

Scalability within a system is its ability to grow by the addition of more resources. It is about making your business grow eventually with time.

Elasticity is about managing the resource dynamics in real-time based on the system requirements. It's about handling multiple traffic spikes frequently in a shorter time duration.

#2: Expectation: "Scalability alone can handle resources without performance degradation."

Reality:

It's not just about system scalability when it comes to business growth. It's a long-term process that requires many cost-effective measures to operate constantly. If a business will be more predictable, you can make better plans to align with your business goals. Proper scalability requires pre-planning for resource allocation and infrastructure design.

Implementing best practices for a scalable system architecture, like vertical/horizontal scaling, will help you out in such cases. If not done proficiently, it can lead to cost overruns, ultimately making an inefficient system.

#3: Expectation: "Elasticity can achieve fully automated scaling without any workload challenges."

Reality:

Elasticity serves as a top database solution for handling high traffic, but it comes with its automation challenges. For example, if accurate monitoring techniques will not be involved, then elasticity may not be able to potentially optimize complex workloads. It needs cost-efficient auto-scaling rules and regulations to minimize your budget.

Frequent scaling events within a system lead to unexpected bills and cost volatility. Even if you're adopting high-defining techniques like resource provisioning, it may lead to latency, further leading to performance bottlenecks.

#4: Expectation: “Scalability can work automatically as soon as businesses adopt cloud computing.”

Reality:

It's not over when you adopt the cloud computing techniques for the system; actually, the work starts from there. Maintaining a system's scalability requires prompt planning and configuration with budget constraints in mind. Cloud computing, though, makes the scaling possible to a significant extent, but it's not fully automatic, to be honest.

Ask any expert of a reputed software product engineering company; they will tell you the reality in detail. First, you must configure the right auto-scaling guidelines according to your system and then the right scaling model. Continuous system monitoring and testing are applicable to align with the scalability needs.

If your system code and architecture are not concurrent, then even the heftiest of cloud servers aren't going to save you.

#5: Expectation: “Elasticity always saves a lot of business money.”

Reality:

Relying on elasticity will not always save you tons of money; it can go the other way with too much overconfidence!

System elasticity helps avoid data overprovisioning, but only when it's fully configured and planned. Poorly configured data causes frequent scaling, resulting in driving up the system costs. Elasticity planning must be done in a way that all the system resources could be utilized to their full extent. Reserving the resources but not utilizing them will waste your expenditure.

So, plan to couple your system elasticity with smart resource management, or else be ready to pay hefty bills on system scaling more often.

#6: Expectation: "Scaling and system elasticity can provide instant flexibility and infinite growth within the system."

Reality:

It requires much effort to maintain this expectation and make it a reality with time. Achieving great system flexibility works slowly, not instantly. It requires a robust, strategic business architecture to adhere to the latest market processes, smart business optimization, and ongoing business trends.

Both system scalability and elasticity hold a modern approach in cloud strategies, but they aren't any magic wands either, meaning proper planning is needed for sure to achieve both milestones. The key lies in building a highly adaptable system with the latest cloud computing system that embraces automation, statelessness, and modularity.

#7: Expectation: “Everything can be scaled easily.”

Reality:

It's a half-truth, as easy scaling is only limited to a few system components, initially. Other components don't scale that easily and require significant effort to perform such tasks. For example, you can scale stateless components with ease by applying horizontal scaling methods. In a microservice architecture, the services are loosely coupled, making space for many databases, caches, and file storages to scale better horizontally.

Whereas the same cannot be elastically scaled that easily, the third-party integrations can also become an issue if not controlled or managed. You must adopt the latest cloud-native system architecture to meet your users' demands eventually. hence, understanding the real capabilities of the system is important, and more important is to understand its limitations!

#8: Expectation: “Scalability is costly, and it is only for large enterprises to afford.”

Reality:

Scalability is a term introduced for small startups to eventually grow bigger with regular adaptation. They can benefit the most from continuous work on business scalability and elasticity. You just need to know the right scaling metrics, such as—

MVP and product-market fit standards

Cost-efficient system growth

Effortless system accountability planning to avoid replanning

Expert tip: The earlier the adoption, the better the scalability!

Businesses need to align their cloud strategies with the relevant system scalability and elasticity techniques. System scalability provides a more predictable business growth, whereas elasticity offers better management with work fluctuations. Based on workload patterns and the needs of the time, businesses must optimize their cloud strategies.

Scalability Vs. Elasticity In Cloud Computing: Major Differences

Concept

System Scalability

System Elasticity

Working

Adds resources to handle workloads.

Adjust resources to handle work demands.

Timing

According to increased work demands

Real-time dynamic scaling

Resource Allocation

Manually

Automatically

Response Time

Time-taking implementations

Instantaneous

Work load Management

Manually

Automated

Cost Effectiveness

Leads to over-provisioning

Enabled cost optimization

System Flexibility

Flexible but not instantaneous.

Instant and automated resource adjustments.

System Efficiency

Underutilized resources due to low demand

Optimized resource utilization

System Complexity

Simple to manage

Requires automation and monitoring

Use Cases

Best for systems with predictable patterns

Best for applications with unpredictable and highly variable workloads

CTA 2:

System scalability or elasticity—We Do Both!

Acquaint Softech is a leading software product engineering company that excels in availing top-rated affordable business solutions for your digital brand. Just let us know your requirements, and we'll blend classy system scaling techniques to create the most result-driven product!

Let's talk!

Conclusion—The Future of Scalability and Elasticity

Rethinking the future of scalability vs. elasticity in cloud computing is vital as the world progresses toward serverless resource provisioning. Automatic scaling-based platforms like AWS, Google Cloud, and Azure offer immense scaling utilities as per the resources, helping developers avoid overprovisioning. It is indeed one of the most cost-effective choices you can ever make for your business!

Understanding that both system scalability and elasticity are needed in order to maintain a legacy system well is important. Both approaches are necessary as AI and ML both are now giving tough competition in these cases. If you are keen to know more, simply contact a software professional to get much more detailed insight.

FAQs

1. What does scalability mean in cloud computing?

A system's core ability to manage increased workloads without sacrificing its functioning is called system scalability. It is required in every software product development legacy system to remain market-fit for a longer time.

2. What does system elasticity mean in cloud computing?

A system's ability to dynamically adjust the resources based on the system's demand and need is termed system elasticity. It basically allows the system to auto-scale up and down as required when demand increases or decreases, for optimizing business costs.

3. Can I use scalability and elasticity together within my legacy system?

Absolutely, you can easily implement either option's scalability vs. elasticity in a cloud computing system, or both, accordingly. You must consult a software engineering expert to learn more about the issue and how to implement both system scalability and elasticity well within the system.

4. What are the best tools for infrastructure scaling and elasticity?

There are many scalability tools for system infrastructure and elasticity. You must first know the exact system requirements and then figure out which tech stack will prove to be the best solution for your business. For example, to get the best system scalability, you can consult top experts and learn more details about load balancing and cloud storage solutions. Plus, top-rate system elasticity tools include Auto-scaling groups (AWS), and monitoring systems like CloudWatch.

[diagnozabam]

AMD construiește în Franța un centru AI de ultimă generație, în parteneriat cu G42 și DataOne

AMD și partenerii săi din Emiratele Arabe Unite și Franța au anunțat la AI Action Summit construirea unuia dintre cele mai performante centre de calcul pentru inteligență artificială din Franța. Centrul va fi construit în Grenoble, în parteneriat cu G42 (prin divizia sa Core42) și DataOne, și va deveni operațional până la mijlocul anului 2025. AMD și Inteligența Artificială: o infrastructură de…

[mkcecollege]

As this synergy grows, the future of engineering is set to be more collaborative, efficient, and innovative. Cloud computing truly bridges the gap between technical creativity and practical execution. To Know More: https://mkce.ac.in/blog/the-intersection-of-cloud-computing-and-engineering-transforming-data-management/

[zomb13s]

The Evolution and Impact of Supercomputers and Servers in the Modern World

Introduction Supercomputers represent the pinnacle of computational power, evolving from massive, room-sized machines to sleek, compact devices with immense processing capabilities. These advancements have drastically transformed scientific research, industry, and even daily life. In parallel, server technology has undergone a rapid evolution, supporting the backbone of global networks and data…

[fdrpjournals]

The Indian Journal of Computer Science and Technology (INDJCST) is a peer-reviewed academic journal that delivers cutting-edge research and advancements in computer science to scientists, engineers, and technology professionals.

It highlights the latest findings across four key domains: computing (including theory, scientific, cloud, and high-performance computing), systems (such as database, real-time, and operating systems), intelligence (covering robotics, bioinformatics, and artificial intelligence), and applications (encompassing security, networking, software engineering, and more).

[buysellram]

BuySellRam.com is expanding its focus on AI hardware to meet the growing demands of the industry. Specializing in high-performance GPUs, SSDs, and AI accelerators like Nvidia and AMD models, BuySellRam.com offers businesses reliable access to advanced technology while promoting sustainability through the recycling of IT equipment. Read more about how we're supporting AI innovation and reducing e-waste in our latest announcement:

[faultfalha]

Industries have long leveraged high performance computing to help solve complex challenges, but the technological landscape is constantly changing. In order to stay ahead of the competition, businesses must adopt the latest tools and technologies to solve their most pressing problems. One such tool is high performance computing, which can help companies achieve their goals quickly and efficiently. By using high performance computing in conjunction with other cutting-edge technologies, businesses can solve complex challenges and stay ahead of the curve.

[dzamie]

Detecting AI-generated research papers through "tortured phrases"

So, a recent paper found and discusses a new way to figure out if a "research paper" is, in fact, phony AI-generated nonsense. How, you may ask? The same way teachers and professors detect if you just copied your paper from online and threw a thesaurus at it!

It looks for “tortured phrases”; that is, phrases which resemble standard field-specific jargon, but seemingly mangled by a thesaurus. Here's some examples (transcript below the cut):

profound neural organization - deep neural network

(fake | counterfeit) neural organization - artificial neural network

versatile organization - mobile network

organization (ambush | assault) - network attack

organization association - network connection

(enormous | huge | immense | colossal) information - big data

information (stockroom | distribution center) - data warehouse

(counterfeit | human-made) consciousness - artificial intelligence (AI)

elite figuring - high performance computing

haze figuring - fog/mist/cloud computing

designs preparing unit - graphics processing unit (GPU)

focal preparing unit - central processing unit (CPU)

work process motor - workflow engine

facial acknowledgement - face recognition

discourse acknowledgement - voice recognition

mean square (mistake | blunder) - mean square error

mean (outright | supreme) (mistake | blunder) - mean absolute error

(motion | flag | indicator | sign | signal) to (clamor | commotion | noise) - signal to noise

worldwide parameters - global parameters

(arbitrary | irregular) get right of passage to - random access

(arbitrary | irregular) (backwoods | timberland | lush territory) - random forest

(arbitrary | irregular) esteem - random value

subterranean insect (state | province | area | region | settlement) - ant colony

underground creepy crawly (state | province | area | region | settlement) - ant colony

leftover vitality - remaining energy

territorial normal vitality - local average energy

motor vitality - kinetic energy

(credulous | innocent | gullible) Bayes - naïve Bayes

individual computerized collaborator - personal digital assistant (PDA)

[hairupintheair]

Trolls Band Together Commentary Notes Part 4/4

- The scene after Branch storms out of Rhonda and Poppy goes after him was the most expensive scene for the computers to handle in the whole movie, even including Mount Rageous, because the clouds in the background are all made of individual fibers that the computers had to calculate while animating. - Commentary quote: "There's so many fibers in this movie. Don't ever commit a crime. All the fibers. They'll trace it right back to you." - They decided on a cloud backdrop because it seemed a good transition between the roads they were traveling in Rhonda and Mount Rageous, which is supposed to be a city up in the sky. The monotone colors and calm landscape of the clouds also fit the scene well, with Branch and Poppy having this serious and emotional heart-to-heart, and it allowed the audience to completely focus on Branch and Poppy without a busy background to be distracting. - Originally the finale was going to take place inside the mentioned Rage Dome. They had a plan for a huge flashy concert performance with the trolls breaking in, but it was coming off as too similar to the ending of the previous movie, so instead they came up with the idea of the vehicle chase through the streets of Mount Rageous. - There was a brief idea early on of Gristle accidentally getting sprayed with trolls' talent from one of V&V's shoulder pad things and momentarily turning into what they called "hot Gristle." Thankfully that was scrapped very quickly. - The Broppy kiss moment was big and they worked on it for a long time, knowing how important it was for the story and the fans. They were going to have effects with big flashy lights (like the pinky promise and the high five of the previous movie), but in the end they decided they wanted to keep it simple and honest. The had the crowd show such a positive reaction to let the audience know what an important moment this was. - The ending after the climax was purposely designed to be a mirror of the opening scene: behind the curtains right before a concert is about to start, but now they've all grown and are supporting each other. - The NSYNC surprise cameo had to be done completely in secret and it was very difficult to keep it under wraps, including changing the names of computer files and keeping audio and visuals separate, etc. It was stressful for the teams working on it, but it paid off spectacularly. - When they were coming up with the idea of the NSYNC reunion, everyone was so excited. Justin pitched it and got the go ahead, then they got the rest of the band members on a ZOOM call to discuss it with them. They were all chatting together and catching up and it was taking way too long, someone realized they were approaching the 45 minute mark (ZOOM calls have a 45 min limit before the program will drop the call) with ten seconds left. So they lost the call mid-sentence and then had to connect everyone again to actually finish pitching the idea to the guys.

[miachel-brown]

How 3D Scanners Revolutionize Archaeology

As a science that explores human history and culture, archaeology has always relied on excavation and manual recording to preserve artifacts and cultural relics. However, this discipline has ushered in a technological revolution in recent years. The introduction of 3D scanning technology has changed the traditional way of working, making archaeological research more efficient, accurate and interactive. With its unique non-contact measurement method, 3D scanners have completely changed the way archaeologists obtain, store and analyze data, bringing great changes to cultural heritage protection.

How 3D Scanning Works 3D scanners capture point cloud data on the surface of an object through lasers, structured light or other sensors to generate a three-dimensional model of the object. These devices can perform high-precision scans of objects of various materials, from tiny artifacts to entire archaeological sites, and can be digitally preserved. Different types of scanners have their own advantages. For example, laser scanners are suitable for large-scale field recording, while structured light scanners are more suitable for high-precision scanning of delicate artifacts.

Accurately capture the details of artifacts Traditional archaeological records rely on manual measurement and drawing, which is not only time-consuming and labor-intensive, but also prone to errors. 3D scanners can quickly and accurately record every detail of the artifact and generate accurate digital copies. For example, a fragile piece of pottery may not be able to be moved multiple times, but with 3D scanning technology, only one scan is needed to generate a digital model for research and display. Researchers can observe and analyze artifacts from different angles without touching the original, thereby minimizing potential damage to the artifacts.

For example, the British National Museum used laser 3D scanning technology to record the Rosetta Stone. This method not only preserves the subtle inscriptions on the stone, but also provides a permanent digital reference for future research and education.

Site restoration and virtual display

Another major challenge in archaeology is to restore damaged or partially lost site structures. With 3D scanning technology, archaeologists can scan the remaining parts of the site and simulate its original appearance with the help of computers. This virtual restoration allows researchers to better understand the structure and use of ancient buildings. For example, after the ruins of the ancient Roman city of Pompeii were 3D scanned, researchers were able to simulate its complete layout before the volcanic eruption, providing visitors and scholars with an immersive experience.

3D scanning also enables museums to offer interactive exhibitions. Virtual reality (VR) and augmented reality (AR) technologies combined with 3D scanning data allow visitors to "touch" digitized artifacts and explore the details of archaeological discoveries. This interactive experience stimulates the public's interest in history and culture and improves the effectiveness of education.

Protection and restoration The restoration of cultural relics requires extremely high precision and meticulous craftsmanship, and 3D scanning provides a powerful support tool for the restoration process. Scanning technology can help restorers accurately measure defective parts and generate restoration plans. For example, when repairing the golden mask of Egyptian pharaoh Tutankhamun, the restoration team used high-resolution 3D scanning data to ensure that the restoration process conforms to the original structure and appearance.

At the same time, the digital model generated by 3D scanning can be used as a permanent backup to prevent natural disasters, wars or accidents from causing irreversible damage to cultural relics. For example, the ancient city of Palmyra, which was destroyed during the Syrian civil war, was partially restored through previous 3D scanning data, providing a valuable reference for future restoration work.

Expanding research perspectives 3D scanning technology is not only a tool for recording cultural relics, but also provides new analytical methods for academic research. With the help of computer programs, researchers can measure every detail on the digital model to obtain richer research data. For example, when studying the traces of use of ancient tools, using 3D scanning models can magnify tiny wear details, and can also be used with an all-in-one 3D scanner to help scholars understand the purpose and frequency of use of tools.

In addition, similar artifacts unearthed from different locations can be compared through 3D scanning data to analyze the similarities of their design and manufacturing techniques. This method provides a new perspective for the study of cross-regional cultural exchanges.

Unlimited possibilities in the future With the continuous development of 3D scanning technology, its application in archaeology will become more extensive

[allaboutkeyingo]

SQL Server 2022 Edition and License instructions

SQL Server 2022 Editions:

• Enterprise Edition is ideal for applications requiring mission critical in-memory performance, security, and high availability

• Standard Edition delivers fully featured database capabilities for mid-tier applications and data marts

SQL Server 2022 is also available in free Developer and Express editions. Web Edition is offered in the Services Provider License Agreement (SPLA) program only.

And the Online Store Keyingo Provides the SQL Server 2017/2019/2022 Standard Edition.

SQL Server 2022 licensing models 

SQL Server 2022 offers customers a variety of licensing options aligned with how customers typically purchase specific workloads. There are two main licensing models that apply to SQL Server:  PER CORE: Gives customers a more precise measure of computing power and a more consistent licensing metric, regardless of whether solutions are deployed on physical servers on-premises, or in virtual or cloud environments. 

• Core based licensing is appropriate when customers are unable to count users/devices, have Internet/Extranet workloads or systems that integrate with external facing workloads.

• Under the Per Core model, customers license either by physical server (based on the full physical core count) or by virtual machine (based on virtual cores allocated), as further explained below.

SERVER + CAL: Provides the option to license users and/or devices, with low-cost access to incremental SQL Server deployments.   

• Each server running SQL Server software requires a server license.

• Each user and/or device accessing a licensed SQL Server requires a SQL Server CAL that is the same version or newer – for example, to access a SQL Server 2019 Standard Edition server, a user would need a SQL Server 2019 or 2022 CAL.

Each SQL Server CAL allows access to multiple licensed SQL Servers, including Standard Edition and legacy Business Intelligence and Enterprise Edition Servers.SQL Server 2022 Editions availability by licensing model:  

Physical core licensing – Enterprise Edition 

• Customers can deploy an unlimited number of VMs or containers on the server and utilize the full capacity of the licensed hardware, by fully licensing the server (or server farm) with Enterprise Edition core subscription licenses or licenses with SA coverage based on the total number of physical cores on the servers.

• Subscription licenses or SA provide(s) the option to run an unlimited number of virtual machines or containers to handle dynamic workloads and fully utilize the hardware’s computing power.

Virtual core licensing – Standard/Enterprise Edition 

When licensing by virtual core on a virtual OSE with subscription licenses or SA coverage on all virtual cores (including hyperthreaded cores) on the virtual OSE, customers may run any number of containers in that virtual OSE. This benefit applies both to Standard and Enterprise Edition.

Licensing for non-production use 

SQL Server 2022 Developer Edition provides a fully featured version of SQL Server software—including all the features and capabilities of Enterprise Edition—licensed for  development, test and demonstration purposes only.  Customers may install and run the SQL Server Developer Edition software on any number of devices. This is  significant because it allows customers to run the software  on multiple devices (for testing purposes, for example)  without having to license each non-production server  system for SQL Server.  

A production environment is defined as an environment  that is accessed by end-users of an application (such as an  Internet website) and that is used for more than gathering  feedback or acceptance testing of that application.   

SQL Server 2022 Developer Edition is a free product !

[krunal-vyas]

Hire Dedicated Developers in India Smarter with AI

Hire dedicated developers in India smarter and faster with AI-powered solutions. As businesses worldwide turn to software development outsourcing, India remains a top destination for IT talent acquisition. However, finding the right developers can be challenging due to skill evaluation, remote team management, and hiring efficiency concerns. Fortunately, AI recruitment tools are revolutionizing the hiring process, making it seamless and effective.

In this blog, I will explore how AI-powered developer hiring is transforming the recruitment landscape and how businesses can leverage these tools to build top-notch offshore development teams.

Why Hire Dedicated Developers in India?

1) Cost-Effective Without Compromising Quality:

Hiring dedicated developers in India can reduce costs by up to 60% compared to hiring in the U.S., Europe, or Australia. This makes it a cost-effective solution for businesses seeking high-quality IT staffing solutions in India.

2) Access to a Vast Talent Pool:

India has a massive talent pool with millions of software engineers proficient in AI, blockchain, cloud computing, and other emerging technologies. This ensures companies can find dedicated software developers in India for any project requirement.

3) Time-Zone Advantage for 24/7 Productivity:

Indian developers work across different time zones, allowing continuous development cycles. This enhances productivity and ensures faster project completion.

4) Expertise in Emerging Technologies:

Indian developers are highly skilled in cutting-edge fields like AI, IoT, and cloud computing, making them invaluable for innovative projects.

Challenges in Hiring Dedicated Developers in India

1) Finding the Right Talent Efficiently:

Sorting through thousands of applications manually is time-consuming. AI-powered recruitment tools streamline the process by filtering candidates based on skill match and experience.

2) Evaluating Technical and Soft Skills:

Traditional hiring struggles to assess real-world coding abilities and soft skills like teamwork and communication. AI-driven hiring processes include coding assessments and behavioral analysis for better decision-making.

3) Overcoming Language and Cultural Barriers:

AI in HR and recruitment helps evaluate language proficiency and cultural adaptability, ensuring smooth collaboration within offshore development teams.

4) Managing Remote Teams Effectively:

AI-driven remote work management tools help businesses track performance, manage tasks, and ensure accountability.

How AI is Transforming Developer Hiring

1. AI-Powered Candidate Screening:

AI recruitment tools use resume parsing, skill-matching algorithms, and machine learning to shortlist the best candidates quickly.

2. AI-Driven Coding Assessments:

Developer assessment tools conduct real-time coding challenges to evaluate technical expertise, code efficiency, and problem-solving skills.

3. AI Chatbots for Initial Interviews:

AI chatbots handle initial screenings, assessing technical knowledge, communication skills, and cultural fit before human intervention.

4. Predictive Analytics for Hiring Success:

AI analyzes past hiring data and candidate work history to predict long-term success, improving recruitment accuracy.

5. AI in Background Verification:

AI-powered background checks ensure candidate authenticity, education verification, and fraud detection, reducing hiring risks.

Steps to Hire Dedicated Developers in India Smarter with AI

1. Define Job Roles and Key Skill Requirements:

Outline essential technical skills, experience levels, and project expectations to streamline recruitment.

2. Use AI-Based Hiring Platforms:

Leverage best AI hiring platforms like LinkedIn Talent Insightsand HireVue to source top developers.

3. Implement AI-Driven Skill Assessments:

AI-powered recruitment processes use coding tests and behavioral evaluations to assess real-world problem-solving abilities.

4. Conduct AI-Powered Video Interviews:

AI-driven interview tools analyze body language, sentiment, and communication skills for improved hiring accuracy.

5. Optimize Team Collaboration with AI Tools:

Remote work management tools like Trello, Asana, and Jira enhance productivity and ensure smooth collaboration.

Top AI-Powered Hiring Tools for Businesses

LinkedIn Talent Insights — AI-driven talent analytics

HackerRank — AI-powered coding assessments

HireVue — AI-driven video interview analysis

Pymetrics — AI-based behavioral and cognitive assessments

X0PA AI — AI-driven talent acquisition platform

Best Practices for Managing AI-Hired Developers in India

1. Establish Clear Communication Channels:

Use collaboration tools like Slack, Microsoft Teams, and Zoom for seamless communication.

2. Leverage AI-Driven Productivity Tracking:

Monitor performance using AI-powered tracking tools like Time Doctor and Hubstaff to optimize workflows.

3. Encourage Continuous Learning and Upskilling:

Provide access to AI-driven learning platforms like Coursera and Udemy to keep developers updated on industry trends.

4. Foster Cultural Alignment and Team Bonding:

Organize virtual team-building activities to enhance collaboration and engagement.

Future of AI in Developer Hiring

1) AI-Driven Automation for Faster Hiring:

AI will continue automating tedious recruitment tasks, improving efficiency and candidate experience.

2) AI and Blockchain for Transparent Recruitment:

Integrating AI with blockchain will enhance candidate verification and data security for trustworthy hiring processes.

3) AI’s Role in Enhancing Remote Work Efficiency:

AI-powered analytics and automation will further improve productivity within offshore development teams.

Conclusion:

AI revolutionizes the hiring of dedicated developers in India by automating candidate screening, coding assessments, and interview analysis. Businesses can leverage AI-powered tools to efficiently find, evaluate, and manage top-tier offshore developers, ensuring cost-effective and high-quality software development outsourcing.

Ready to hire dedicated developers in India using AI? iQlance offers cutting-edge AI-powered hiring solutions to help you find the best talent quickly and efficiently. Get in touch today!

[hiteshrivani]

The Rise of 5G and Its Impact on Mobile App Development

5G isn’t just about faster internet — it’s opening up a whole new era for Mobile App Development. With dramatically higher speeds, ultra-low latency, and the ability to connect millions of devices seamlessly, 5G is transforming how developers think about building apps. From richer experiences to smarter services, let's explore how 5G is already reshaping the mobile app landscape in 2025 and beyond.

1. Lightning-Fast Data Transfer

One of the biggest promises of 5G is incredibly fast data transfer — we're talking about speeds up to 100 times faster than 4G. For mobile apps, this means that large files, high-resolution images, and HD or even 4K video content can be downloaded or streamed instantly. Apps that once needed to compress their data heavily or restrict features due to bandwidth limits can now offer fuller, richer experiences without worrying about lag.

2. Seamless Real-Time Experiences

5G dramatically reduces latency, meaning the time between a user action and the app’s response is almost instant. This will revolutionize apps that rely on real-time communication, such as video conferencing, live-streaming platforms, and online gaming. Developers can create much more responsive apps, allowing users to interact with data, people, and services with zero noticeable delay.

3. The Growth of AR and VR Mobile Applications

Augmented Reality (AR) and Virtual Reality (VR) apps have been growing, but 5G takes them to another level. Because of the high bandwidth and low latency, developers can now build more complex, interactive, and immersive AR/VR experiences without requiring bulky hardware. Imagine trying on clothes virtually in real-time or exploring a vacation destination through your phone — 5G is making this possible within Mobile App Development.

4. Smarter IoT-Connected Apps

The Internet of Things (IoT) will thrive even more in a 5G environment. Smart home apps, connected car apps, fitness trackers, and other IoT applications will be able to sync and update faster and more reliably. Developers can now integrate complex IoT ecosystems into mobile apps with minimal worries about network congestion or instability.

5. Enhanced Mobile Cloud Computing

Thanks to 5G, mobile cloud computing becomes much more viable. Instead of relying solely on local device storage and processing, apps can now store large amounts of data and execute processes directly in the cloud without latency issues. This allows users with even mid-range smartphones to experience high-performance features without the need for powerful hardware.

6. Revolutionizing Mobile Commerce

E-commerce apps are set to benefit greatly from 5G. Instant-loading product pages, real-time customer support, virtual product previews through AR, and lightning-fast payment gateways will enhance user experience dramatically. This could lead to higher conversion rates, reduced cart abandonment, and greater user loyalty in shopping apps.

7. Opportunities for New App Categories

With the technical limitations of mobile networks reduced, a whole new range of apps becomes possible. Real-time remote surgeries, autonomous vehicles controlled via mobile apps, and highly advanced telemedicine solutions are just a few examples. The doors are open for mobile developers to innovate and create applications that were previously impossible.

8. Better Security Requirements

With 5G’s mass connectivity also comes a bigger responsibility for security. As mobile apps become more connected and complex, developers must prioritize data protection, encryption, and secure authentication methods. Building security deeply into Mobile App Development workflows will be critical to maintain user trust.

9. More Demanding User Expectations

As 5G rolls out globally, users will expect every app to be faster, smoother, and more capable. Apps that fail to leverage the benefits of 5G may seem outdated or sluggish. This shift will push developers to continually optimize their apps to take advantage of higher speeds and smarter networking capabilities.

10. Preparing for the 5G Future

Whether you’re building entertainment apps, business solutions, healthcare tools, or gaming platforms, now is the time to adapt to 5G. Developers must start thinking about how faster speeds, greater device connections, and cloud capabilities can improve their mobile applications. Partnering with experts in Mobile App Development who understand the full potential of 5G will be key to staying ahead in a rapidly evolving digital world.