#Rent GPU server for AI

[letrune]

"ai"s, another rant

Consider: what is the product? Most of these "ais" (large language models) are "free", but you get only a few rounds for free. It's like a casino, you ask a thing, get images, and you can roll again if you liked it enough.

There are many of these LLMs that say in their TOS that they may save, sell and base their new generations on the images you produced. That they will access your computer data, save it, may even sell it. Some even proposed to use your own computing power, CPU and GPU for these.

But the money comes from somewhere - namely, bitcoin, nft sales and now, premium generation with ai, and lending them out for rent to companies. This is where the LLM companies get their money. The way they can replace artists, and get whatever they want, even if it breaks the law or worse.

Many articles rely on fake news dreamt up by a LLM textparser. Fake images circulate. Many dictators love to doctor images, and now thry got it even faster. Truth is being harder to find when it is easier to fake.

The product is you. Your gambling addiction. Your artistic efforts. Your truth. Everything the internet was meant for. All of it is now for rent, for sale, and to be reimagined by techbros who don't understand the systems they want to ruin as long as it makes them money.

Consider again: bitcoin ruin the economy of the little people and make a few rich. Nfts ruin online markets and videogames, and make a few rich. "Ai" ruin art and text and news, and make a few rich.

There is nothing to be gained in it. It is a toy for a bunch of gambling addicts in the 5% who want to be the 1%, and now, thanks to many big companies taking these, the tool for megacorps to get even richer by spending even less.

Imagine, Warner Brothers gets their own. They can start producing a movie, announce it, then can it, delete it and start anew. No spending beyond paying the energy and water bill and the server costs, but there are no people involved. They can produce for anyone, remove any piece, use any bodies, living and dead, for anything, from selling slop to playing the big bad. They have to spend less and you got to pay the same or more. Why would they even finish any movie? Just produce a slop, toss on a streaming service, then remove and make more, half of them go for tax refunds anyway.

It is a tool for instant gratification for you, and then more cash for the top? Yes. It is.

[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?

[sharon-ai]

Revolutionizing AI Development with Affordable GPU Cloud Pricing and Flexible Cloud GPU Rental Options

In today’s data-driven world, the demand for high-performance computing is growing at an unprecedented pace. Whether you’re training deep learning models or running complex simulations, access to powerful GPUs can make all the difference. Fortunately, modern platforms now offer cost-effective GPU cloud pricing and flexible cloud GPU rental services, making cutting-edge computing accessible to everyone, from startups to research institutions.

Why Affordable GPU Cloud Pricing Matters

Efficient GPU cloud pricing ensures that businesses and developers can scale their operations without incurring massive infrastructure costs. The ability to access high-end GPUs on a pay-as-you-go model is especially beneficial for AI workloads that require intensive computation.

Budget-Friendly Rates: Platforms are now offering some of the most competitive pricing models in the industry, with hourly rates significantly lower than traditional hyperscalers.

No Hidden Fees: Transparent pricing with no data transfer charges allows users to control their budget while maximizing performance fully.

Diverse GPU Options: From advanced NVIDIA A100s to AMD's latest offerings, users can choose from various GPUs to meet their unique workload requirements.

Cloud GPU Rental: The Key to Flexibility

Cloud GPU rental empowers users to access the right hardware at the right time. This flexibility is ideal for project-based work, startups testing AI models, or research teams running simulations.

On-Demand Access: Users can rent GPUs exactly when they need them—scaling up or down depending on their workflow.

Scalable Solutions: From single-user tasks to enterprise-level needs, modern platforms accommodate all scales of usage with ease.

Secure and Reliable: Enterprise-grade infrastructure housed in Tier III and IV data centers ensures minimal downtime and maximum performance.

Cost-Effective Performance at Your Fingertips

One of the biggest advantages of cloud GPU rental is the massive cost savings. Modern providers offer rates up to 50% lower than traditional cloud platforms, making them an ideal choice for budget-conscious teams.

All-Inclusive Pricing: What you see is what you pay—no extra charges for data transfer or system maintenance.

Tailored for AI & HPC: These platforms are built from the ground up with AI, deep learning, and HPC needs in mind, ensuring high-speed, low-latency performance.

Custom Discounts: Users with long-term needs or bulk usage requirements can take advantage of volume discounts and custom plans.

Designed for Developers and Innovators

Whether you’re building the next breakthrough AI application or analyzing large-scale scientific data, cloud GPU rental services offer the tools you need without the overhead.

Virtual Server Configuration: Customize your virtual environment to fit your project, improving efficiency and cutting waste.

Integrated Cloud Storage: Reliable and scalable cloud storage ensures your data is always accessible, secure, and easy to manage.

Final Thoughts

The landscape of high-performance computing is changing rapidly, and access to affordable GPU cloud pricing and flexible cloud GPU rental is at the heart of this transformation. Developers, researchers, and enterprises now have the freedom to innovate without being held back by hardware limitations or financial constraints. By choosing a provider that prioritizes performance, transparency, and flexibility, you can stay ahead in a competitive digital world.

[lowendbox]

In today’s tech landscape, the average VPS just doesn’t cut it for everyone. Whether you're a machine learning enthusiast, video editor, indie game developer, or just someone with a demanding workload, you've probably hit a wall with standard CPU-based servers. That’s where GPU-enabled VPS instances come in. A GPU VPS is a virtual server that includes access to a dedicated Graphics Processing Unit, like an NVIDIA RTX 3070, 4090, or even enterprise-grade cards like the A100 or H100. These are the same GPUs powering AI research labs, high-end gaming rigs, and advanced rendering farms. But thanks to the rise of affordable infrastructure providers, you don’t need to spend thousands to tap into that power. At LowEndBox, we’ve always been about helping users find the best hosting deals on a budget. Recently, we’ve extended that mission into the world of GPU servers. With our new Cheap GPU VPS Directory, you can now easily discover reliable, low-cost GPU hosting solutions for all kinds of high-performance tasks. So what exactly can you do with a GPU VPS? And why should you rent one instead of buying hardware? Let’s break it down. 1. AI & Machine Learning If you’re doing anything with artificial intelligence, machine learning, or deep learning, a GPU VPS is no longer optional, it’s essential. Modern AI models require enormous amounts of computation, particularly during training or fine-tuning. CPUs simply can’t keep up with the matrix-heavy math required for neural networks. That’s where GPUs shine. For example, if you’re experimenting with open-source Large Language Models (LLMs) like Mistral, LLaMA, Mixtral, or Falcon, you’ll need a GPU with sufficient VRAM just to load the model—let alone fine-tune it or run inference at scale. Even moderately sized models such as LLaMA 2–7B or Mistral 7B require GPUs with 16GB of VRAM or more, which many affordable LowEndBox-listed hosts now offer. Beyond language models, researchers and developers use GPU VPS instances for: Fine-tuning vision models (like YOLOv8 or CLIP) Running frameworks like PyTorch, TensorFlow, JAX, or Hugging Face Transformers Inference serving using APIs like vLLM or Text Generation WebUI Experimenting with LoRA (Low-Rank Adaptation) to fine-tune LLMs on smaller datasets The beauty of renting a GPU VPS through LowEndBox is that you get access to the raw horsepower of an NVIDIA GPU, like an RTX 3090, 4090, or A6000, without spending thousands upfront. Many of the providers in our Cheap GPU VPS Directory support modern drivers and Docker, making it easy to deploy open-source AI stacks quickly. Whether you’re running Stable Diffusion, building a custom chatbot with LLaMA 2, or just learning the ropes of AI development, a GPU-enabled VPS can help you train and deploy models faster, more efficiently, and more affordably. 2. Video Rendering & Content Creation GPU-enabled VPS instances aren’t just for coders and researchers, they’re a huge asset for video editors, 3D animators, and digital artists as well. Whether you're rendering animations in Blender, editing 4K video in DaVinci Resolve, or generating visual effects with Adobe After Effects, a capable GPU can drastically reduce render times and improve responsiveness. Using a remote GPU server also allows you to offload intensive rendering tasks, keeping your local machine free for creative work. Many users even set up a pipeline using tools like FFmpeg, HandBrake, or Nuke, orchestrating remote batch renders or encoding jobs from anywhere in the world. With LowEndBox’s curated Cheap GPU List, you can find hourly or monthly rentals that match your creative needs—without having to build out your own costly workstation. 3. Cloud Gaming & Game Server Hosting Cloud gaming is another space where GPU VPS hosting makes a serious impact. Want to stream a full Windows desktop with hardware-accelerated graphics? Need to host a private Minecraft, Valheim, or CS:GO server with mods and enhanced visuals? A GPU server gives you the headroom to do it smoothly. Some users even use GPU VPSs for game development, testing their builds in environments that simulate the hardware their end users will have. It’s also a smart way to experiment with virtualized game streaming platforms like Parsec or Moonlight, especially if you're developing a cloud gaming experience of your own. With options from providers like InterServer and Crunchbits on LowEndBox, setting up a GPU-powered game or dev server has never been easier or more affordable. 4. Cryptocurrency Mining While the crypto boom has cooled off, GPU mining is still very much alive for certain coins, especially those that resist ASIC centralization. Coins like Ethereum Classic, Ravencoin, or newer GPU-friendly tokens still attract miners looking to earn with minimal overhead. Renting a GPU VPS gives you a low-risk way to test your mining setup, compare hash rates, or try out different software like T-Rex, NBMiner, or TeamRedMiner, all without buying hardware upfront. It's a particularly useful approach for part-time miners, researchers, or developers working on blockchain infrastructure. And with LowEndBox’s flexible, budget-focused listings, you can find hourly or monthly GPU rentals that suit your experimentation budget perfectly. Why Rent a GPU VPS Through LowEndBox? ✅ Lower CostEnterprise GPU hosting can get pricey fast. We surface deals starting under $50/month—some even less. For example: Crunchbits offers RTX 3070s for around $65/month. InterServer lists setups with RTX 4090s, Ryzen CPUs, and 192GB RAM for just $399/month. TensorDock provides hourly options, with prices like $0.34/hr for RTX 4090s and $2.21/hr for H100s. Explore all your options on our Cheap GPU VPS Directory. ✅ No Hardware CommitmentRenting gives you flexibility. Whether you need GPU power for just a few hours or a couple of months, you don’t have to commit to hardware purchases—or worry about depreciation. ✅ Easy ScalabilityWhen your project grows, so can your resources. Many GPU VPS providers listed on LowEndBox offer flexible upgrade paths, allowing you to scale up without downtime. Start Exploring GPU VPS Deals Today Whether you’re training models, rendering video, mining crypto, or building GPU-powered apps, renting a GPU-enabled VPS can save you time and money. Start browsing the latest GPU deals on LowEndBox and get the computing power you need, without the sticker shock. We've included a couple links to useful lists below to help you make an informed VPS/GPU-enabled purchasing decision. https://lowendbox.com/cheap-gpu-list-nvidia-gpus-for-ai-training-llm-models-and-more/ https://lowendbox.com/best-cheap-vps-hosting-updated-2020/ https://lowendbox.com/blog/2-usd-vps-cheap-vps-under-2-month/ Read the full article

[computer8920]

Cloud GPUs: The Rise of GPU-as-a-Service for AI and Gaming

The demand for high-performance computing has skyrocketed in recent years, driven by advancements in artificial intelligence (AI), machine learning (ML), and gaming. Traditional on-premise GPUs, while powerful, come with significant costs and limitations. Enter GPU-as-a-Service (GPUaaS), a cloud-based solution that is revolutionizing how industries access and utilize GPU power. Here’s why Cloud GPUs are becoming the go-to choice for AI and gaming:

1. What Are Cloud GPUs?

Cloud GPUs are virtualized graphics processing units hosted on remote servers and accessible via the internet. Providers like AWS, Google Cloud, Microsoft Azure, and NVIDIA DGX offer scalable GPU resources on demand. Users can rent GPUs by the hour, eliminating the need for expensive hardware investments.

2. Benefits of GPU-as-a-Service

Cloud GPUs offer several advantages:

Cost-Effective: No upfront costs for purchasing and maintaining physical GPUs.

Scalability: Easily scale up or down based on project requirements.

Accessibility: Available to anyone with an internet connection, democratizing access to high-performance computing.

Maintenance-Free: Cloud providers handle hardware updates, cooling, and power management.

3. Applications in AI and Machine Learning

Cloud GPUs are transforming AI and ML workflows:

Training AI Models: They accelerate the training of deep learning models, reducing computation time from weeks to hours.

Inference: Real-time AI applications, such as natural language processing and computer vision, benefit from low-latency GPU performance.

Research: Universities and startups can access cutting-edge GPU resources without massive budgets.

4. Gaming on Cloud GPUs

Cloud GPUs are also reshaping the gaming industry:

Cloud Gaming Platforms: Services like NVIDIA GeForce NOW, Google Stadia, and Xbox Cloud Gaming leverage Cloud GPUs to stream high-quality games to any device.

No Hardware Limitations: Gamers can play AAA titles on low-end devices, as rendering happens on remote GPUs.

Cross-Platform Play: Seamlessly switch between devices without losing progress.

5. Challenges and Future Outlook

While Cloud GPUs offer immense potential, there are challenges to address:

Latency: Internet speed and stability remain critical for real-time applications.

Cost Management: While cost-effective, prolonged usage can add up, requiring careful budgeting.

Future Trends: Expect advancements in edge computing and 5G to further enhance Cloud GPU performance and accessibility.

In conclusion, Cloud GPUs are transforming industries by making high-performance computing more accessible, affordable, and scalable. Whether you’re training AI models or gaming on the go, GPU-as-a-Service is paving the way for a more connected and efficient future.

Want to Buy Computer Parts in Bulk at Affordable Prices From VSTL?

Would you like to purchase computer components in bulk without breaking the bank? Look no further! Value Smart Trading Limited is your trusted partner for affordable computer parts. Whether you're a business, educational institution, or reseller, they offer a wide range of products, including motherboards, CPUs, GPUs, storage devices, and peripherals, all at competitive wholesale prices. Known for reliability and excellent customer service, this provider ensures seamless bulk purchasing, fast delivery, and customized solutions tailored to your needs. Save time and money while equipping your setup with top-tier hardware—choose them for all your bulk computer part requirements!

[seedfinder01]

AI Seed Phrase Finder

Bitcoin private key finder is additional module to the main AI Seed Phrase Finder program, which allows you to generate private keys and the full BTC address based on a given Bitcoin address pattern.

Bitcoin Private key generator

This allows you to find private keys to BTC addresses previously created by other peoples with “Bitcoin Vanity Address” method.

➡️To do this, the program, using the power of a rented Supercomputer and GPU servers with hardware configured for these tasks, generates an infinite number of addresses and private keys for them, which allows you to literally select the key to the Bitcoin address of interest👍

✔️But the most interesting thing is that the “balance checking module” is capable of checking with 🖥High speed, using private keys to the accompanying addresses obtained based on a given template, the presence of a balance💰 on these Bitcoin wallets and displaying them in the “program log” and, of course, saving them in a text file.

The brilliance of the system lies in its ability to employ a diverse range of algorithms and methods, each carefully crafted to optimize the generation process and streamline the validation of potential private keys. From brute force techniques to probabilistic algorithms, the software navigates through the vast landscape of possible keys with remarkable speed and accuracy.

As the software navigates through the immense space of potential private keys, it employs a series of automated checks to swiftly eliminate invalid options and focus on those with the highest likelihood of containing a positive balance. Through a combination of intelligent analysis and rapid iteration, the system identifies and verifies viable keys with remarkable efficiency, unlocking access to the coveted resources hidden within bitcoin addresses.

✅By leveraging complex mathematical algorithms, “AI Private Key Finder” can generate highly secure and unique keys for bitcoin addresses. Once a potential private key is generated, the software then automatically checks the corresponding bitcoin address for any positive balance. This process involves connecting to the blockchain network and querying the balance of the address to determine if any bitcoins are associated with it.

[numgenius-ai]

Is Numgenius Al real or fake?

Numgenius Ai is the market leader in low-cost cloud GPU rental. Numgenius AI can also make money online. Numgenius AI will bear the server operating costs by raising funds, and investors will gain profits from the servers.

In the rapidly evolving world of technology, passive income has become more than a buzzword—it's a tangible goal for many. The rise of artificial intelligence (AI) and the omnipresence of cloud computing have opened new avenues for earning online. One such opportunity comes from Numgenius AI, a leader in the field of cloud GPU rental and bespoke enterprise server solutions. Here, we explore how Numgenius AI is redefining the landscape of server leasing and how you can profit from this trend.

Introduction to Numgenius AI Founded in 2010, Numgenius AI has distinguished itself as a technology pioneer, offering cloud-based GPU rental services and custom server solutions. With over 3,000 positive reviews, we have carved a niche in the server rental and cloud computing market. Our services cater to a wide range of client needs, from startups to tech giants, providing affordable and tailored AI solutions. Numgenius AI is more than just a service provider; We are at the forefront of innovation in artificial intelligence and cloud computing, integrating technology with sustainability and social responsibility.

Understanding Server Leasing Server leasing is an integral part of Numgenius AI's offerings. It's a model that allows businesses to rent AI-optimized servers, offering a cost-effective alternative to purchasing expensive hardware.

Key benefits of server leasing include:

Cost Savings: Reduces the need for significant upfront investments in hardware. Access to High-End Technology: Leases provide access to the latest AI-optimized servers with powerful GPUs. Flexibility and Scalability: Easily scale server needs up or down, adapting to changing business requirements. Maintenance and Upgrades: Maintenance and upgrading responsibilities are typically handled by the lessor, easing the burden on businesses.

Generating Income with Numgenius AI Investing in Numgenius AI's server leasing model can be a lucrative venture. By leasing servers, you're entering the growing market of cloud GPU rental, a sector in high demand. This model offers a stable and predictable return on investment. Numgenius AI utilizes investor funds to build and maintain servers, pay for operational costs, and then rents these servers to other companies, sharing the profits with investors.

Numgenius AI FAQ Why Do Companies Rent Servers? Companies opt for server rentals due to the high costs associated with building and maintaining their own servers.

How Does Numgenius AI Use Investor Funds? Investments are used for building servers, covering operational expenses like electricity and labor.

How Does Numgenius AI Profit? Numgenius AI generate income by renting out the servers built with investor funds to other companies on a daily, weekly, or monthly basis.

Conclusion Numgenius AI offers a unique opportunity to turn technology into a passive income source. By investing in our server rental model, you not only earn money; You are becoming part of the AI infrastructure revolution. The business is more than just a financial investment, it's a step into a future where technology, innovation and revenue intersect.

Whether you're a tech enthusiast or an investor looking for new horizons, Numgenius AI offers a safe and intelligent pathway. Join the cloud computing revolution, and Unlocking the future of artificial intelligence.

[demigirlboss2girlrotboogaloo]

I have this Ars Technica article pinned in my clipboard now because of how often I've had to cite it as a sort of accessible primer to why the oft-cited numbers on AI power consumption are, to put it kindly, very very wrong. The salient points of the article are that the power consumption of AI is, in the grand scheme of datacenter power consumption, a statistically insignificant blip. While the power consumption of datacenters *has* been growing, it's been doing so steadily for the past twelve years, which AI had nothing to do with. Also, to paraphrase my past self:

While it might be easy to look at the current massive AI hypetrain and all the associated marketing and think that Silicon Valley is going cuckoo bananas over this stuff long term planning be damned, the fact is that at the end of the day it's engineers and IT people signing off on the acquisitions, and they are extremely cautious, to a fault some would argue, and none of them wanna be saddled with half a billion dollars worth of space heaters once they no longer need to train more massive models (inference is an evolving landscape and I could write a whole separate post about that topic).

Fundamentally, AI processors like the H100 and AMD's Instinct MI300 line are a hedged bet from all sides. The manufacturers don't wanna waste precious wafer allotment on stock they might not be able to clear in a year's time, and the customers don't wanna buy something that ends up being a waste of sand in six months time once the hype machine runs out of steam. That's why these aren't actually dedicated AI coprocessors, they're just really really fucking good processors for any kind of highly parallel workload that requires a lot of floating point calculations and is sensitive to things like memory capacity, interconnect latencies, and a bunch of other stuff. And yeah, right now they're mainly being used for AI, and there's a lot of doom and gloom surrounding that because AI is, of course, ontologically evil (except when used in ways that read tastefully in a headline), and so their power consumption seems unreasonably high and planet-destroying. But those exact same GPUs, at that exact same power consumption, in those same datacenters, can and most likely *will* be used for things like fluid dynamics simulations, or protein folding for medical research, both of which by the way are usecases that AI would also be super useful in. In fact, they most likely currently are being used for those things! You can use them for it yourself! You can go and rent time on a compute cluster of those GPUs for anything you want from any of the major cloud service providers with trivial difficulty!

A lot of computer manufacturers are actually currently developing specific ML processors (these are being offered in things like the Microsoft copilot PCs and in the Intel sapphire processors) so reliance on GPUs for AI is already receding (these processors should theoretically also be more efficient for AI than GPUs are, reducing energy use).

Regarding this, yes! Every major CPU vendor (and I do mean every one, not just Intel and AMD but also MediaTek, Qualcomm, Rockchip,and more) are integrating dedicated AI inference accelerators into their new chips. These are called NPUs, or Neural Processing Units. Unlike GPUs, which are Graphics Processing Units (and just so happen to also be really good for anything else that's highly parallel, like AI), NPUs do just AI and nothing else whatsoever. And because of how computers work, this means that they are an order of magnitude more efficient in every way than their full-scale GPU cousins. They're cheaper to design, cheaper to manufacture, run far more efficiently, and absolutely sip power during operation. Heck, you can stick one in a laptop and not impact the battery life! Intel has kind of been at the forefront of these, bringing them to their Sapphire Rapids Xeon CPUs for servers and workstations to enable them to compete with AMD's higher core counts (with major South Korean online services provider Naver Corporation using these CPUs over Nvidia GPUs due to supply issues and price hikes), and being the first major vendor to bring NPUs to the consumer space with their Meteor Lake or first generation Core Ultra lineup (followed shortly by AMD and then Qualcomm). If you, like me, are a colossal giganerd and wanna know about the juicy inside scoop on how badly Microsoft has screwed the whole kit and caboodle and also a bunch of other cool stuff, Wendell from Level1Techs has a really great video going over all that stuff! It's a pretty great explainer on just why, despite the huge marketing push, AI for the consumer space (especially Copilot) has so far felt a little bit underwhelming and lacklustre, and if you've got the time it's definitely worth a watch!

I don't care about data scraping from ao3 (or tbh from anywhere) because it's fair use to take preexisting works and transform them (including by using them to train an LLM), which is the entire legal basis of how the OTW functions.

[colebradley]

The best way to rent GPU server

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How LetzAI empowered creativity with scalable, high-performance AI infrastructure - AI News

New Post has been published on https://thedigitalinsider.com/how-letzai-empowered-creativity-with-scalable-high-performance-ai-infrastructure-ai-news/

How LetzAI empowered creativity with scalable, high-performance AI infrastructure - AI News

LetzAI is quickly becoming a go-to platform for high-quality AI-generated images. With a mission to democratise and personalise AI-powered image generation, it has emerged as one of the most popular and high-quality options on the market.

The problem:

In 2023, Neon Internet CEO and co-founder Misch Strotz was struck by a clever idea: give Luxembourg residents the power to easily generate local images using AI. Within a month, Luxembourg-focused LetzAI V1 went live.

Encouraged by strong local demand, Strotz and his team began working on a global version of the platform. The vision? An opt-in AI platform empowering brands, creators, artists, and individuals to unlock endless creative possibilities by adding their own images, art styles, and products. “Other AI platforms scrape the internet, incorporating people and their content without permission. We wanted to put the choice and power in each person’s hands,” Strotz explains.

Before long, the team began working on V2. In addition to generating higher quality and more personalised AI-generated images, V2 would drive consistency across objects, characters, and styles. After uploading their own photos and creating their own models, users can blend them with other models created by the community to create an endless number of unique images.

However, LetzAI faced a significant hurdle in training and launching V2 – a global GPU shortage. With limited resources to train its models, LetzAI needed a reliable partner to help evolve its AI-driven platform and keep it operating smoothly.

The solution:

In the search for a fitting partner, Strotz spoke to major vendors including hyperscalers and various Europe-based providers. Meeting Gcore’s product leadership team made the decision clear. “It was amazing to meet executives who were so knowledgeable about technology and took us seriously,” recalls Strotz.

Gcore’s approach to data security and sovereignty further solidified the decision. “We needed a trusted partner who shared our commitment to European data protection principles, which we incorporated into the development of our platform” he continues.

The result:

LetzAI opted for Gcore’s state-of-the-art NVIDIA H100 GPUs in Luxembourg. “This was the perfect option, allowing us to keep our model training and development local. With Gcore, we can rent GPUs rather than entire servers, making it a far more cost-effective solution by avoiding unnecessary costs like excess storage and idle server capacity,” Strotz explains. This approach provided flexibility, efficiency, and high performance, tailored specifically for AI workloads.

LetzAI was able to adapt its app to run in containers, configure model training tasks to run on GPU Cloud, and use Everywhere Inference for image generation and upscaling. “Everywhere inference reduces the latency of our output and enhances the performance of AI-enabled apps, allowing us to optimise our workflows for more accurate, real-time results,” Strotz says.

In just two months, LetzAI V2 launched to serve users around the world. And Strotz and team were already developing its successor.

Empowering creativity with scalable, high-performance AI infrastructure

With Gcore’s continued support, LetzAI quickly deployed V3. “The Gcore team was incredibly responsive to our needs, guiding us to the best solution for our evolving

requirements. This has given us a powerful and efficient infrastructure that can flex according to demand,” says Strotz.

Running V3 on Gcore means LetzAI users experience fast, reliable performance. Artists, individuals, and brands are already putting V3 to use in interesting ways. For example, in response to what LetzAI calls its ‘AI Challenges’, a Luxembourg restaurant chain prompted residents to create thousands of images using its model of a pizza.

In another example, LetzAI teamed with digital agency LOOP to dress PUMA’s virtual influencer and avatar, Laila, in a Moroccan soccer jersey. According to Strotz, “PUMA had struggled to make clothing look realistic on Laila. When they saw our images, they said the result was 1,000 times better than anything they had tried.”

That wasn’t the only brand intrigued by V3’s possibilities. After LetzAI posted V3-generated images of models wearing Sloggi underwear, Sloggi’s creative agency STAN Studios asked LetzAI to generate more images for market testing.

Always looking for new ways to support creators, LetzAI also launched its Image Upscaler feature, which enhances images and doubles their resolution. “Our creators can now resolve common AI image issues around quality and resolution. Everywhere Inference is pivotal in delivering the power and speed needed for these dynamic image enhancements,” noted Strotz.

Platform evolution and AI innovation without limits

As its models exceed user expectations worldwide, LetzAI can rely on Gcore to handle a high volume of requests. Confident about generating a limitless number of high-quality images on the fly, LetzAI can continue to scale rapidly to become a sustainable, innovation-driven business.

“As we further evolve—such as by adding video features to our platform – our partnership with Gcore will be central to LetzAI’s continued success,” Strotz concluded.

Photo by Tim Arterbury on Unsplash

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Stable Diffusion VR is a startling vision of the future of gaming

A while ago I spotted someone working on real time AI image generation in VR and I had to bring it to your attention because frankly, I cannot express how majestic it is to watch AI-modulated AR shifting the world before us into glorious, emergent dreamscapes.  Applying AI to augmented or virtual reality isn’t a novel concept, but there have been certain limitations in applying it—computing power being one of the major barriers to its practical usage. Stable Diffusion image generation software, however, is a boiled-down algorithm for use on consumer-level hardware and has been released on a Creative ML OpenRAIL-M licence. That means not only can developers use the tech to create and launch programs without renting huge amounts of server silicon, but they’re also free to profit from their creations. I was awoken in the middle of the night to conceptualize this project Scotty Fox – Stable Diffusion VR dev ScottieFoxTTV (opens in new tab) is one creator who’s been showing off their work with the algorithm in VR on twitter. “I was awoken in the middle of the night to conceptualize this project,” he says. As a creator myself, I understand that the Muses enjoy striking at ungodly hours. What they brought to him was an amalgamation of Stable Diffusion VR and TouchDesigner (opens in new tab) app-building engine, the results of which he refers to as “Real-time immersive latent space.” That might sound like some hippie nonsense to some, but latent space is a concept fascinating the world right about now.  At a base level, it’s a phrase that in this context describes the swelling potential that artificial intelligence brings to augmented reality as it pulls ideas together from the vastness of the unknown. While it’s an interesting concept, it’s one for a feature at a later date. Right now I’m interested in how Stable Diffusion VR manages to work so well in real time without turning any consumer GPU (even the recent RTX 4090 (opens in new tab)) into a smouldering puddle. Stable Diffusion VR Real-time immersive latent space. Small clips are sent from the engine to be diffused. Once ready, they’re queued back into the projection.Tools used:https://t.co/UrbdGfvdRd https://t.co/DnWVFZdppT#aiart #vr #stablediffusionart #touchdesigner #deforum pic.twitter.com/x3QwQDkapTOctober 11, 2022 See more (Image credit: ScottieFoxTTV) “Diffusing small pieces into the environment saves on resources,” Scotty explains. “Small clips are sent from the engine to be diffused. Once ready, they’re queued back into the projection.” The blue boxes in the images here show the parts of the image being worked on by the algorithm at any one time. It’s a much more efficient way to have it working in real time. Anyone who’s used an online image generation tool will understand that a single image can take up to a minute to create, but even if it does take a little while to work on each individual section, the results still feel like they’re happening immediately as you’re not focusing waiting for a single image to finish diffusing. And although not at the level of photorealism as they may one day be, the videos Scotty’s been posting are utterly breathtaking. Flying fish in the living room, ever-shifting interior design ideas, lush forests and nightscapes evolving before your eyes. With AI able to make projections onto our physical world in real time, there is so much potential for use in the gaming space. Midjourney CEO David Holz describes the potential for games to one day be “dreams” (opens in new tab) and it certainly feels like we’re moving hastily in that direction. Though, the important next step is navigating the minefield that is the copyright and data protection issues (opens in new tab) arising around the datasets that algorithms like Stable Diffusion trained on.  Source link Originally published at Melbourne News Vine

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Google Cloud Will Not Be Able To Overtake Microsoft Azure

New Post has been published on https://perfectirishgifts.com/google-cloud-will-not-be-able-to-overtake-microsoft-azure/

Google Cloud Will Not Be Able To Overtake Microsoft Azure

ANKARA, TURKEY – MARCH 3: In this photo illustration a mobile phone and computer screens display … [] Microsoft and Google logos in Ankara, Turkey on March 3, 2020. Halil Sagirkaya / Anadolu Agency

Google Cloud certainly has the technical chops and engineering talent to compete with Microsoft Azure and Amazon’s AWS when it comes to cloud infrastructure, edge computing – and especially inferencing/training for machine learning models. However, Google may lack focus due to Search and YouTube being the main revenue drivers. This is seen from the company’s inability to ignite revenue growth in the cloud segment during a year when digital transformation has been accelerated by up to six years due to work-from-home orders.

In this analysis, we discuss why Google (Alphabet) may have missed a critical window this year for the infrastructure piece. We also analyze how Microsoft directed all of its efforts to successfully close the wide lead by AWS. Lastly, we look at how all three companies will bring the battle to the edge in an effort to maintain market share in this secular and fiercely competitive category.

Cloud IaaS Overview:

The three leading hyperscalers in the United States have diverse origins. Amazon found itself serendipitously holding server space year-round that it could rent out and was first to market by a wide lead. Amazon continues to release customization tools and cloud services for developers at a fast clip and this past week was no exception.  

Microsoft’s roots in enterprise created a direct path to upsell on-premise and become the leader in hybrid. The majority of the Fortune 500 is on Azure as they want seamless security and APIs regardless of the environment.

Google is one of the largest cloud customers in the world due to its search engine and mass-scale consumer apps, and therefore, is often first to create cloud services and architectures internally that later lead to widespread adoption, such as Kubernetes. Machine learning is another piece where Google was one of the first to require ML inference for mass-scale models.

Despite all three having very talented teams of engineers and various areas of strength, we see AWS maintain its lead and Microsoft Azure firmly hold the second-place spot. Keep in mind that Azure launched one year after Google Cloud yet has 3X the market share and is growing at a higher percentage.

Google Cloud grew two percentage points from 5% to 7% since 2018 while Azure grew four percentage points from 15% to 19% in the same period. In the past year, Google Cloud saw a 1% gain compared to Azure’s 2% gain, according to Canalys.

Azure is under Intelligent Cloud but the company does break down the growth rate which was 48%. Although Google Cloud Is not specifically broken down, the Google Cloud segment grew 45% year-over-year compared to Microsoft Azure up 48% year-over-year.

Amazon Web Services is growing at 29%, which is substantial considering the law of large numbers. In the past two quarters, Google Cloud reported 43% year-over-year growth and 52% in the quarter before that. Microsoft has seen a slightly less deceleration from 51% and this is down from the 80%-range almost two years ago.

The key thing here is that when Microsoft held the percentage of market share that GCP currently holds, Azure was growing in the 80-90% range. This is the range we should be seeing from Google Cloud if the company expects to catch up to Azure.

In 2020, the term “digital transformation” has become a buzzword with cloud companies seeing up to six years of acceleration. Nvidia is a bellwether for this with triple-digit growth in the data center segment in both Q2 and Q3. Despite this catalyst, Google has lagged the category in Q2 and Q3 in terms of both growth and percentage share of market. If there were any year that Google Cloud could pull ahead, it should have been this year.

Alphabet has emphasized that GCP is a priority and the company will be “aggressively investing” in the necessary capex. However, the window of opportunity was wide open this year and aggressive investments would ideally have been allocated during the years of 2017-2018 to stave off Azure’s high-growth years with 80-90%.

Google is Capable but Lacks Focus

There is no argument that Alphabet is an innovator within cloud and a leader in its own right. Across public, private and hybrid cloud, containers are used by 84% of companies and 78% of those are managed on Kubernetes – which has risen in popularity along with cloud-native apps, microservices architectures and an increase in APIs. Kubernetes was first created by Google engineers as the company ran everything in containers internally and this was powered by an internal platform called Borg which generated up to 2 billion container deployments a week. This led to automated orchestration rather than manual and also forced a new architecture away from monolithic as server-side changes were required.

Kubernetes also helps with scaling as it allows for scaling of the container that needs more resources instead of the entire application. Microservices dates back to Unix, while Kubernetes, the automation piece around containers, is what Google engineers invented before releasing it to the Cloud Native Foundation for widespread adoption.

Just as Google was one of the first to need automated orchestration for containerization of cloud-native apps, the company was also one of the first to require low-power machine learning workloads. The compute intensive workloads were running on Nvidia’s GPUs for both training and inferencing until Google made their own processing unit called Tensorflow (TPUs) to perform the workload at a lower cost and higher performance.

Performance between TPUs and GPUs is often debated depending on the current release (A100 versus fourth-generation TPUs is the current battle). However, the TPU does have an undisputed better performance per watt for power-constrained applications. Notably, some of this comes with the territory of being an ASIC, which is designed to do one specific application very well whereas GPUs can be programmed as a more general-purpose accelerator. In this case, the benchmarks where TPUs compete are object detection, image classification, natural language processing and machine translation – all areas where Google’s product portfolio of Search, YouTube, AI assistants, and Google Maps, for example, excels.

Notably, TPUs are used internally at Google to help drive down the costs and capex of its own AI and ML portfolio and they are also available to users of Google’s AI cloud services. For example, eBay adopted TPUs to build a machine learning solution that could recognize millions of product images.

Unless Google releases an internal technology as open-source, it won’t be adopted by the competitors. This is where Nvidia’s agnosticism becomes a positive as it’s universally used by Amazon, Microsoft, Google —- and Alibaba, Baidu, Tencent, IBM and Oracle. Meanwhile, TPUs create vendor lock in which most companies want to avoid in order to get the best capabilities across multiple cloud operators (i.e. multi-cloud). eBay is the exception here as the company needs Google-level object detection and image classification.

In a similar vein of Google being early to the company’s internal requirements, BigQuery is also a superior data warehouse system that competes with Snowflake (I cover Snowflake with an in-depth analysis here). BigQuery has a serverless feature that makes it easier to begin using the data warehouse as the serverless feature removes the need for manual scaling and performance tuning. Dremel is the query engine for BigQuery.

BigQuery has a strong following with nearly twice the number of companies as Snowflake and is growing around 40%. Due to AWS being a first mover and having a large cloud IaaS market share, Redshift has the biggest market presence but growth is nearly flat at 6.5%.

Point being, Google has important areas of strength and first-hand experience – whether it’s in data analytics, machine learning/inference or cloud-native applications at scale. Google’s search engine and other applications are often the first globally to challenge current architectures and inferencing capabilities.

However, as we see in the contrast between Google and Microsoft in the most recent earnings calls, Google has a hard time prioritizing cloud over the bigger revenue drivers. Meanwhile, Microsoft has a no holds barred approach with one, singular focus: Azure.

Q3 Earnings Calls

The most recent earnings calls from both Microsoft and Google could not have carried more contrast. Google focused primarily on search and YouTube while adding towards the last half of the call that GCP is where the majority of their investments and new hires were directed. Notably, one analyst wondered if the capex investments would eat at margins and produce enough returns. 

Microsoft, on the other hand, held an hour-long call that was nearly all-Azure including what the company is doing right now to capture more market share, a laundry list of large enterprises coming on board and strategic partnerships to strengthen its second place standing. The company’s beginning, middle and end was Azure and cloud services.

Here is a preview of how the two opened:

Thanks for joining us today. This quarter, our performance was consistent with the broader online environment. It’s also testament to the investment we’ve made to improve search and deliver a highly relevant experience that people turn to for help in moments big and small. We saw an improvement in advertiser spend across all geographies, and most of verticals, with the world accelerating its transition to online and digital services. In Q3, we also saw strength in Google Cloud, Play and YouTube subscriptions.

This is the third quarter we are reporting earnings during the COVID-19 pandemic. Access to information has never been more important. This year, including this quarter showed how valuable Google’s founding Product Search has been to people. And importantly, our products and investments are making a real difference as businesses work [indiscernible] and get back on their feet. Whether it’s finding the latest information on COVID-19 cases in their area, which local businesses are open, or what online courses will help them prepare for new jobs, people continue to turn to Google search.

You can now find useful information about offerings like no contact delivery or curbside pickup for 2 million businesses on search and maps. And we have used Google’s Duplex AI Technology to make calls to businesses and confirm things like temporary closures. This has enabled us to make 3 million updates to business information globally.

We know that people’s expectations for instant perfect search results are high. That’s why we continue to invest deeply in AI and other technologies to ensure the most helpful search experience possible. Two weeks ago, we announced a number of search improvements, including our biggest advancement in our spelling systems in over a decade. A new approach to identifying key moments and videos, and one of people’s favorites hum to search which will identify a song noticed based on the humming. –Sundar Pichai, Q3 2020 Earnings Call

Compare this to the tone for Microsoft’s earnings call …

We’re off to a strong start in fiscal 2021, driven by the continued strength of our commercial cloud, which surpassed $15 billion in revenue, up 31% year-over-year. The next decade of economic performance for every business will be defined by the speed of their digital transformation. We’re innovating across the full modern tech stack to help customers in every industry improve time to value, increase agility, and reduce costs.

Now, I’ll highlight examples of our momentum and impact starting with Azure. We’re building Azure as the world’s computer with more data center regions than any other provider, now 66, including new regions in Austria, Brazil, Greece, and Taiwan. We’re expanding our hybrid capabilities so that organizations can seamlessly build, manage, and deploy their applications anywhere. With Arc, customers can extend Azure management and deploy Azure data services on-premise, at the edge, or in multi-cloud environments.

With Azure SQL Edge, we’re bringing SQL data engine to IoT devices for the first time. And with Azure Space, we’re partnering with SpaceX and SES to bring Azure compute to anywhere on the planet.

Leading companies in every industry are taking advantage of this distributed computing fabric to address their biggest challenges. In energy, both BP and Shell rely on our cloud to meet sustainability goals. In consumer goods, PepsiCo will migrate its mission critical SAP workloads to Azure. And with Azure for Operators, we’re expanding our partnership with companies like AT&T and Telstra, bringing the power of the cloud and the edge to their networks. Just last week, Verizon chose Azure to offer private 5G mobile edge computing to their business customers.  -Satya Nadella, Fiscal Q1 2021 Earnings (Calendar Year Q3 2020)

The calls continue in a similar manner with Microsoft making it clear they have their entire weight behind cloud while Google must continue to cater to its largest revenue drivers – search and consumer. The main takeaway we get from the call is that Google is investing in GCP rather than a takeaway of market dominance or growth. Here are a few examples:

As we’ve told you on these calls, given the progress we’re making, and the opportunity for Google Cloud in this growing global market, we continue to invest aggressively to build our go-to-market capabilities, execute against our product roadmap, and extend the global footprint of our infrastructure … And another: An obvious example is Cloud. We do intend to maintain a high level of investment, given the opportunity we see. That includes the ongoing increases in our go-to-market organization, our engineering organization, as well as the investments to support the necessary capex. So, hopefully, that gives you a bit more color there. And, also here … And the point that both Sundar and I have underscored is that we are investing aggressively in Cloud, given the opportunity that we see. And, frankly, the fact that we were later relative to peers, we’re encouraged, very encouraged, by the pace of customer wins and the very strong revenue growth in both GCP and Workspace. We do intend to maintain a high level of investment to best position ourselves. And I kind of went through some of those items, the go-to-market team, the engineering team, and capex. And so we describe this as a multi-year path because we do believe we’re still early in this journey.

The question remains if aggressively investing will have the same impact after the digital transformation has been accelerated by up to six years. Nobody could have predicted covid and the work-from-orders but we see from the growth rates on large revenue bases that AWS and Azure were better positioned to answer the demand.

Edge Computing: No rest for the weary

The race for cloud IaaS dominance is only beginning and the hyperscalers are not resting on their laurels as they compete for the edge. Major strategic partnerships are being struck with telecom companies to break open new uses cases for decentralized applications and increased connectivity. Google mentioned Nokia in their earnings call while Microsoft mentioned AT&T, Verizon and Telstra. Amazon also has partnerships with Verizon and Vodafone. (For brevity sake, you can assume every telecom company is either partnered or will be partnering with multiple hyperscalers for edge computing).

Here is a breakdown of the buildout and how these strategic partnerships plan to profit from 5G. The result will be new use cases, such as remote surgery, autonomous vehicles, AR/VR and a significant number of internet of things devices that aren’t feasible with 4G and/or with the current centralized cloud IaaS servers.

AWS Wavelength:

Amazon’s edge computing technologies are being rapidly built-out. For example, Wavelength is being embedded in Vodafone’s 5G networks throughout Europe in 2021 after being in beta for two years. This will provide ultra-low latency for application developers enabled by 5G. On Vodafone’s end, they have developed multi-access edge computing (MEC) to fit both 4G and 5G networks to process data and applications at the edge. This lowers processing time from about 50-200 milliseconds to 10 milliseconds. Amazon is also expanding its Local Zones to offer low-latency in metro areas from L.A. to about a dozen cities in 2021.

In order to support its retail business, AWS built out 200 points of presence where serverless processing like Lambda can run. The network latency map will be enhanced by telco partnerships who have about 150 PoPs per telco.

Microsoft Azure with Edge Zones:

Azure has the largest global footprint across the cloud providers. Where AWS has been the long-standing developer preference, Microsoft is the C-suite/enterprise preferred company across the Fortune 500. Microsoft’s goal will be to move compute closer to end users and to offer Azure-hosted compute and storage as a single virtual network with security and routing.

Microsoft excelled at hybrid as a strategy for taking market share (which I also detailed as the investment thesis for my position in Microsoft after the company missed Q3 2018 earnings and prior to winning the JEDI contract). Azure Edge Zones extends the current hybrid network platform to allow distributed applications to work across on-premise, edge data centers both public and private, Azure IaaS both public and private. This allows the same security and APIs to work seamlessly across these hybrid environments. The overarching performance will attempt to combine the range of compute and storage capabilities of Azure with the speeds/low-latency of the edge.

Google Cloud with Global Mobile Edge Cloud (GMEC):

Google is also partnering with telecom companies such as AT&T to deploy Google hardware inside AT&T’s network edge to run AI/ML models and other software for 5G solutions. Similar to AWS and Azure, the goal is to open up new use cases for industries, such as retail, manufacturing and transportation.

Anthos for Telecom is a Kubernetes-orchestrated infrastructure that can be deployed anywhere including an AWS cluster. In this way, the strategy for Google continues to amplify its strengths which is containerized network functions to merge edge and core infrastructure. This helps with decentralized applications and could potentially compete with “network slices” to where AT&T could potentially use local breakouts to offer a cloud service tier in a few years from now.

Conclusion:

We’ve seen Google build some of the best products for developers in terms of automating microservices and container-orchestration with Kubernetes and also ASIC chips (TPUs) that compete with the likes of Nvidia. I’m not betting against Google’s talented engineers by any means, rather I’m simply observing that the infrastructure piece is leaning towards more of a duopoly at this time. Cloud is expensive on a capex level, so if Google doesn’t find its footing, the margins driven by ads could take a hit in the near-term.

Who will lead software and AI applications is impossible to predict (and when) as the main competitors will be hundreds (if not thousands) of startups. With that said, I personally own Amwell because Google is a backer and I think health care is an example of a vertical where Google’s experience with data can deliver a serious competitive edge. To be clear, Alphabet may have an advantage with AI/ML software whereas this analysis is about the infrastructure. Perhaps there will be a catalyst in the future for Google Cloud to take more share but the strategy is not evident at this time.

Beth Kindig owns shares of Microsoft and Amwell which are mentioned in this analysis. The information contained herein is not financial advice

From Cloud in Perfectirishgifts

[nealtv8]

First flight while using On Air Company plugin. Kind of much like so, On-Air Company is like FSEcomony where you can have it as in any sim that has an economy mode like ATS or ETS2. You can hire employees, buy/rent different airplanes, get FBOs, while you make your airline empire. Depending on the server may differ by difficulty and AI pilot capability. This flight will be from Fort Myers to Naples as this is the first successful leg. Recorded in patch 1. Social: Twitch: https://bit.ly/35N8IHF Non-Gaming Channel: https://www.youtube.com/c/2nealfire Twitch VOD Channel: hyperurl.co/yg8i18 Discord Server: https://bit.ly/2QcI4Ty Imgur: http://bit.ly/2ckec28 Facebook: http://bit.ly/2bPiBMt Twitter: http://bit.ly/2cpGHfa Instagram: http://bit.ly/2c66vRn Steam Profile: http://bit.ly/2bWgCD8 Steam Group: http://bit.ly/2bVD1lw Support: Patreon: https://bit.ly/2m3POFY Green Man Gaming: http://bit.ly/2cqrL3k CD Keys: https://bit.ly/2MIatyd Humble: https://bit.ly/2DPdgTs Donate/Tip: https://bit.ly/2TNoG29 Anonymous Donate/Tip: https://bit.ly/2GhjeN8 Download TubeBuddy: https://bit.ly/2Z0MLVQ Computer Specs: Main PC Specs: CPU: AMD Ryzen 9 3900X 3.8GHz, Dozen-Core CPU Cooler: AMD Wraith Prism Cooler Motherboard: MSI B450 Gaming Pro Carbon AC (AM4 ATX) RAM: G.SKILL TridentZ 32GB (4 x 8GB) DDR4-3200MHz SSD: Samsung 840 EVO 500GB HDD1: Seagate Barracuda 3TB 7200RPM HDD2: Western Digital Black 2TB 7200RPM HDD3: Western Digital Blue 4TB 5400RPM HDD4: Western Digital Black 6TB Case: Thermaltake View 71 RGB GPU: EVGA GeForce RTX 2080 XC2 Ultra ACX ICX2 (8GB GDDR6) PSU: EVGA Supernova 850 G5, 80+ Gold DISP 1: 1x Samsung S24R350 (24"/1080p) DISP 2: 1x ASUS VE248H (24"/1080p) MOUSE: Logitech G502 RGB KB: Corsair K68 (Cherry MX Red) Game Capture: AVerMedia Live Gamer 4K Camera Capture: AVerMedia Live Gamer HD 2 Streaming/Recording PC Specs: CPU: AMD Ryzen 7 1800X 3.6GHz, Octa-Core CPU Cooler: Corsair H100i V2 Motherboard: MSI B350M Mortar (AM4 Micro ATX) RAM: G.Skill Ripjaws V 16GB (2 x 8GB) DDR4-2400MHz HDD1: Western Digital Blue 2TB 5400RPM HDD2: Seagate Barracuda 500GB 7200RPM HDD3: Seagate Barracuda 4TB 5900RPM SSD: Western Digital Black PCIe 256GB GPU: EVGA GeForce GTX 1050 Ti FTW ACX (4GB GDDR5) Case: Thermaltake Core V21 PSU: Corsair RM750x, 80+ Gold Capture: AVerMedia Live Gamer 4K DISP 1: 1x ASUS VE248H (24"/1080p) DISP 2: 1x Sceptre E24 (24"/1080p) MOUSE: MSI Interceptor DS B1 KB: Logitech G710+ (Cherry MX Brown) Video Rendering PC Specs: CPU: Intel Core i7-7700k 4.2 GHz, Quad-Core CPU Cooler: be quiet! Dark Rock Pro 4 Motherboard: Gigabyte AORUS GA-Z270X Gaming 7 (LGA 1151 ATX) RAM: Kingston HyperX Fury Black 16GB (2 x 8GB) DDR4-2133MHz HDD: 2X Western Digital Black 1TB 7200 RPM SSD: Kingston A400 120GB GPU: EVGA GeForce GTX 1070 SC ACX 3.0 Black Edition (8GB GDDR5) Case: NZXT H500 PSU: Seasonic M12II Bronze EVO 850W, 80+ Bronze DISP: Samsung 5 Series J5202 (43"/1080p) KB/M: Logitech K400 Laptop Specs: Dell Precision M4600 CPU: Intel Core i7-2860QM 2.50GHz, Quad-Core GPU: Nvidia Quadro 1000M (1GB) DISP: 1920x1080 Display SSD: SanDisk SSD Plus 480GB HDD: Toshiba Hard Drive 750GB Controllers: Logitech G27 Logitech Extreme 3D Pro Saitek X52 Pro Simu SKRS Thrustmaster TFRP Rail Driver Steam Controller XBOX One Controller Track IR 5 Cameras: Logitech C922 Panasonic HC-V770 Audio: Yamaha MG12XU Cloud Microphones CL-1 Cloudlifter RODE PodMic

[jacobhinkley]

AI, Low Energy Consumption to Make Mining More Profitable

A company from Switzerland, Swiss Alps Energy AG (SAE), is planning to return the mining industry to high profits. The plan includes using clean yet cheap energy sources to feed mining rigs, consuming much less energy. The SAE estimates ROI from its mining operation will be over 200% in the next two years if the prices of cryptocurrencies continue to rise.

The mining industry is an integral part of the blockchain infrastructure. However, it is experiencing certain problems affecting rentability. Mining operations grow ever more complex, which leads to a serious rise of electricity consumption by mining equipment, and the competition for mining rewards is getting tougher. SAE claims to have a solution for the industry’s issues, allowing more profitable mining operation.

Mining cubes running on clean energy

The main unit of the SAE mining system is the SAM Cube, an aluminum case equipped with ASIC or GPU miners. The first units contain mining rigs for the mining of Ethereum and Bitcoin, but this list will be expanded in the future to “practically all cryptocurrencies”.

SAE deploys SAM Cubes high in the Swiss mountains. The annual average temperature at this altitude is below 15 degrees Celsius, so the SAM Cube does not require any energy for air conditioning. In addition, the SAM Cube leverages an Organic Rankine Cycle (ORC) system, which uses the waste heat of processes through a downstream steam power process to generate electricity. The high altitude facilitates energy recovery as it lowers the boiling point of water. SAE claims its ORC system will reduce energy consumption drastically compared to other mining facilities.

To make profits even higher, SAE will also implement SamaiX technology for the mining operation. SamaiX is an AI that calculates the profitability of different coins’ mining on a real-time basis and makes suggestions for miners so that they can adjust their mining accordingly.

The next step for SAE is to build or purchase several small-scale hydroelectric power plants, a popular source of energy in Switzerland. The company sets a goal to use “green” and sustainable sources of energy only. SAE has already started buying electricity at a price of only 5.26 cents per kWh.

High ROI for mining operations

As SAE states, all the listed technologies can increase the profitability of cryptocurrency mining. Calculations made by the company show an annual profit from the current model of the SAM Cube with an output of 7000 TH/s will be more than $300,000 if the price of Bitcoin and Ethereum reaches $10,000 and $1,000 respectively. The cost of power will be as little as $55,000.

At the same time, SAE’s main source of income will be leasing its infrastructure to mining and server facilities. For the capacity of 14 TH/s, SAE charges a fixed fee of $4,000 for two years and a service fee of 10% of the mining revenues.

To sum up, the company expects the ROI of its mining operation to be over 200% in the next two years if the prices of cryptocurrencies continue to rise

SAE blockchain platform as a service

The company emphasizes it will develop decentralized blockchain infrastructure on the basis of SAM mining infrastructure to provide its platform as a service for customers. The company will give customers the opportunity to operate a private decentralized blockchain and develop individual solutions based on distributed ledger technology.

The SAM infrastructure offers an interface to operate a network of blockchain nodes with a VPN. It also can function as a part of the corporate network, and any blockchain technologies that are operated virtually on the SAM infrastructure can be used.

SAM Token

All services provided by SAE as well as the purchase of power and SAM Cubes will be paid in SAM tokens. This includes renting and buying mining units, electricity supply from SAM power plants, and hosting blockchains on decentralized SAM Cubes. The ICO for SAM tokens is scheduled for June 2018.

The post AI, Low Energy Consumption to Make Mining More Profitable appeared first on NewsBTC.

AI, Low Energy Consumption to Make Mining More Profitable published first on https://medium.com/@smartoptions