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HomeLabs can be expensive when it comes to energy costs. It’s easy to accumulate multiple power-hungry servers, networking equipment, and computers.

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HomeLab provides a great environment for learning new technologies, testing software, and exploring your interests hands-on. However, they can also lead to surprisingly high electricity bills if you are not careful. Multiple power-hungry servers, disk arrays, and networking gear can quickly make your HomeLab an energy sinkhole.

Photo by Patrik Kernstock / Unsplash1.1 Avoid Old Multi-CPU Enterprise Servers

Previous generation servers like the Dell R710 or HP DL380 G7 are inefficient due to older architectures and power-hungry components. For example, a dual Intel Xeon server fully loaded with CPUs, RAM, and drives can draw 200-300 watts at idle.

Compare this to a modern system with new low-power CPUs, DDR4 memory, and flash storage that might only pull 50-60 watts while doing nothing. Over months of continuous uptime, that difference in idle power draw adds up on your electricity bill.

1.2 Leverage Single Board Computers for Light Duties

For lightweight network services like DNS, DHCP, or monitoring, a small single-board computer like the Raspberry Pi is a great energy-efficient option. The idle power draw of a Pi is typically around 3-5 watts—a fraction of what even a modern server would use.

Mini PCs with mobile CPUs are another excellent choice when you need more horsepower for simple servers. Just ensure the components are fully solid state with no spinning hard drives.

1.3 Build Home Servers with Latest Desktop CPUs

Recent desktop processors and chipsets incorporate enhanced power-saving C-states compared to enterprise server platforms. While not as robust for heavy workloads, modern desktop CPUs can still deliver excellent performance per watt for typical homelab use cases.

For example, an Intel Core i5 or i7 CPU on a mini ITX motherboard with 16-32GB of RAM can handle quite a bit while staying energy-friendly. Cooling requirements are also reduced compared to hotter enterprise hardware.

1.4 Consolidate Multiple Workloads via Virtualization

Rather than running each application on dedicated physical hardware, use virtual machines to consolidate many homelab services and workloads onto fewer servers. This maximizes the utilization of the underlying host hardware.

For example, you could run a Kubernetes cluster, media server, and NAS in VMs on a single server instead of provisioning separate equipment for each. Done right, virtualization enables energy efficiency without compromising performance.

Photo by Stephen Dawson / Unsplash2.1 Turn Off Idle Virtual Machines

In addition to physical servers, remember to fully shut down your VMs when not needed. Even when paused, VMs still consume a small amount of overhead CPU cycles and memory on the host rather than bare metal.

2.2 Use Wake-on-LAN to Remotely Power On Servers

With Wake-on-LAN enabled on the network card, you can easily power on servers remotely when needed again. This prevents accidentally leaving gear running continuously when forgotten.

You can take this a step further by connecting smart plugs to non-virtualized servers and controlling the power state via home automation platforms. Just take care to safely shut down rather than abruptly cut power.

Photo by Iyus sugiharto / Unsplash3.1 Consolidate Drives with Larger Capacities

Rather than a greater number of smaller disks, select higher-capacity drives (e.g. 10TB, 12TB, etc.) and leverage RAID to get your needed overall volume. This significantly cuts down on the quantity of motors spinning 24/7.

3.2 Avoid Unnecessary Hardware Like RAID Cards

Additional components like SAS HBAs and RAID cards draw noticeably more idle power atop the drives themselves. Carefully evaluate if each extra hardware piece is necessary for your workload and usage before purchasing.

3.3 Use SSDs Strategically For Caching and Speed

Adding a small amount of SSD storage for caching speeds up hard drive response times. In turn, this may allow you to get by with fewer or slower spinning drives to achieve your performance goals.

3.4 Enable Spin down on RAID/HBA for Unused Drives

If some drives are not frequently accessed, enable spin down or standby mode to have them spin down after a period of inactivity. This can shave off a few watts when disks are idle. Just ensure performance needs are met.

Photo by Jordan Harrison / Unsplash4.1 Old PCIe Cards Prevent CPU Power Saving States

Legacy PCIe cards and add-in boards can prevent modern processors from entering their deepest sleep C-states, costing extra power. When possible, choose newer energy-efficient models with native OS support.

4.2 Minimize Extreme Networking Standards Like 10GbE

Leading-edge networking standards like 10Gbps Ethernet equipment have notoriously high power demands, often over 10 watts per port while idle. For most homelabs, evaluate if 1GbE may be suitable rather than always opting for the latest and greatest.

4.3 Use Adaptive/Auto-Sizing Power Supplies

Power supplies are most efficient when lightly loaded, around 50% or less of maximum capacity. Choose units with auto-sensing outputs that provide only the necessary wattage. Avoid grossly overprovisioning.

Conclusion

A Quick Tip: Avoid overprovisioning resources and you can achieve solid performance for the majority of homelab workloads while also saving substantially on your electricity bill over the long term

Make it a habit to evaluate actual needs versus wants before every hardware purchase. Right-size components for the job and your efficiency will pay dividends down the road. Your homelab experiments will still be amply powered while not increasing your utility costs.

Monolithic architectures accelerate time-to-market, while Microservices are more suited for longer-term flexibility and maintainability at a substantial scale.

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As software systems scale in complexity, architects must decide whether a monolithic or microservices architecture is the best choice. This decision greatly impacts system scalability, fault tolerance, ease of development, and more for years to come.

1. Monolithic Architecture

In a monolithic design, all critical application components are combined into a single, tightly integrated unit. The components are heavily dependent on each other and communicate via language-level interfaces. The entire software system scales and is deployed as one. Performance can be greatly optimized via shared state and function calls between components. The data model is enforced in a single database.

2. Microservices Architecture

The software is split into multiple smaller independent services in a microservices architecture. Each service contains the logic and data to handle specific capabilities. The services run their own processes and communicate via APIs. The services can be developed, tested, deployed, and scaled independently allowing for greater flexibility at the cost of higher network reliability requirements and added complexity of data consistency.

→ Monolithic

With a monolithic architecture, the various social media functions would be developed together as components of the same system, using the same languages and frameworks. The components would be deployed as a single unit, with all data in the same databases.

→ Microservices

In a microservices model, each social media function becomes a standalone service, owning its own logic and databases. Services scale independently from each other allowing optimization. An API Gateway sits in front handling routing across services. Tradeoffs exist due to added latency between services and the complexity of data replication.

4. What are the Architectural Tradeoffs?

Architecture
✅ Pros
❌ Cons

Monolithic
– Development Speed – Performance Optimizations – Data Consistency
– Massive Codebase Complexity – Lack of Independent Scalability

Microservices
– Independent Scaling and Deployment – Fault Isolation – Technology Flexibility
– Higher Operational Complexity – Data Consistency Challenges

5. What Works Best Monolithic or Microservices?

Suppose, a small company may be developing a new web application to support customer surveys and analytics. There is a single development team and the planned scalability needs are fairly basic for the initial product. Here are factors that would make a monolithic architecture suitable:

For small startup companies, faster and simpler development with fewer developers is critical. Microservices add complexity unneeded at their size.The core components (UI, business logic, database) can scale together linearly. No need to decouple scaling needs yet.Tight coupling between survey input, analysis, and reporting features actually improves the product experience.

However, if the application becomes very successful and usage grows 10x, a switch to microservices could then occur to allow independent scaling since the monolith may no longer fit scalability needs:

Scale the survey collection capability separately from analysis features.Runtime traffic spikes on analytical features won’t impact data collection consistency, and vice versa.Replace the UI framework without rewriting the entire app if needed.Onboard more developers by dividing the workload across services.When Monolithic Works Well?

Monolithic architectures allow for rapid development cycles which can enable faster innovation. If future scale is limited and teams are small, monolithic provides benefits. It works well for linear scalability needs and simpler applications where rapid reliable communication between components is required.

When Microservices Excel?

For large enterprise systems that need to operate reliably at a massive scale, microservices ease the burden. Independent deployment eases change management burdens. If technology flexibility is desired per component and complex data replication needs are met, microservices can excel.

Kubernetes is an open-source system that helps with deploying, scaling, and managing containerized applications.

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So you keep hearing about this “Kubernetes” thing but have no idea what it is or does? No worries. I’m here to explain Kubernetes to you in simple terms so you have a basic understanding of what all the fuss is about.

Let’s start from the beginning. Kubernetes is an open-source system that helps with deploying, scaling, and managing containerized applications. Hmm, containerized applications – that probably sounds like more tech jargon if you’re new to all this stuff.

Imagine you have a sweet lemonade stall stand that has become wildly popular. You make the best lemonade in town and suddenly tons of people are showing up thirsty for a cup. But there’s only one of you managing the entire stand!

You need help meeting all the lemonade demands. So you put up a job ad for lemonade sellers and get many applicants. Now you can hire more people and set up multiple lemonades stands around the neighbourhood. Everyone works together following your special lemonade recipes and processes.

In software terms, this means:The lemonade stand is like a software application.You, the owner, are like the Cluster Manager.The lemonade sellers are like multiple Containers running instances of your software app.The job ad and hiring process is like a Controller that can spin up more Containers.

Okay, but managing all these new lemonade stands, sellers, inventory orders etc. quickly becomes complicated!

You bring in your friend Kube (short for Kubernetes) to rescue the situation. Kube takes over all the heavy operational work also Know as features of Kubernetes:Make sure each stand has enough lemons, cups etc (Resources)Telling stands to open or close at the right times (Scaling)Building more stands to handle customer demand (Provisioning)Monitoring for any issues and fixing them (Self-Healing)

With Kube’s help, you can now focus on the fun stuff – coming up with new lemonade flavours!

And that, my thirsty friend, is Kubernetes – an efficient “conductor” that orchestrates containers, resources and services so you can focus on creating awesome applications!

So the question is- “What even is Kubernetes?”

Well, remember our lemonade stand from before?

Let’s Say we package our secret lemonade recipe 🍋 into containers 📦 for easy transport. But demands are rising! 📈 We quickly replicate many stands 🏪🏪🏪 by spinning up containerized lemonade copies fast! 💨

Panic! Now we have too many stands! 😱 Enter Kube, our cluster manager! Kube helps:

🔹 Deploy containerized apps (lemonade stands) across nodes (servers) 🖥️🔹 Monitor everything and self-heal crashes 🛑🚑🔹 Automatically scale up or down based on traffic 📉 📈🔹 Efficiently allocate resources to pods (stand groups) ⚖️

Important Key Concepts That You Should Know:Kubernetes Architecture

🟡 Pods: Grouped containers with shared resources 📦📦🟡 Nodes: Networked servers for processing work 🖥️🖥️🟠 Deployments: Blueprint for pods across nodes 📜🟢 Services: Networking to connect deployed pods 🗃️🟣 Ingress: Entry points into a cluster for traffic 🚪

Quick Overview of Kubernetes Magic!One morning, a cute cat video goes viral overnight 😸🌟 Driving tons of thirsty customers to our East side lemonade stands as they leave home to share the cute cat link! 📱🏃‍♂️🏃‍♀️➡️🍋Kube monitors traffic and capacity on all nodes from his Kubernetes control centre 👨‍💻👀 Sees a huge spike in customers at East stands! 📈 📈He decides to scale up more lemonade pods to handle demand, protecting stability 🆙More pods => more power to make super yummy lemonades! 🍋✨Oh no, bad luck! Node-4 housing Stand-3 crashes due to surging traffic! 💥😱Kube initiates self-healing – rapidly recreates needed pods on available healthy nodes 🚑🤕➡️🆗Our hero Kube keeps optimizing resource allocation to sustain smooth operations as customer patterns shift! ⚖️🛠

Lemonades flowing again! 🍋✨🎉🙌 And that is Kubernetes in action! From zero to hero in minutes with Kube’s magic! ✨😎

Many startups initially launch on AWS or other public clouds because it allows rapid scaling without upfront investments. But as these companies grow, the operating costs steadily rise.

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These two Companies OneUptime and Prerender are finding that migrating from Amazon Web Services (AWS) to bare metal servers hosted in colocation data centres can lead to substantial cost savings. They have cut costs by over 50% by taking control of their infrastructure while maintaining performance and reliability.

OneUptime was spending $456K+ annually on a 28-node AWS Kubernetes cluster.Prerender projected over $1M per year on AWS services and data transfer.📈 The Hidden Costs of Cloud

For many startups and tech companies, AWS seems like an easy choice. It allows you to spin up servers and scale rapidly without investing in your hardware upfront. But as these companies grow, the operating costs on AWS start to add up:

Monthly bills for instances, storage, data transfer, load balancing services, etc. can exceed $100,000+Data transfer costs in particular can be astronomical – $0.08+/GB in some regionsNo control over hardware – “noisy neighbour” problems can affect performanceVendor lock-in reduces infrastructure flexibility

Bare metal servers refer to physical servers that are dedicated to a single tenant. In contrast, virtual machines (VMs) are virtualized compute instances that run on shared hardware.

➕ Benefits of Bare Metal ServersCost – Bare metal eliminates the hidden fees and unpredictable billing associated with the cloud. You pay for the resources you use, with no additional charges for things like egress traffic or API calls.Performance – Bare metal allows full access to resources for more consistent high workload performance like high I/O requirementsControl – Bare metal provides granular hardware control and customizationIsolation – Dedicated single-tenant resources, so no “noisy neighbour” problemsScalability – VMs allow fast automated scaling, bare metal has more limited flexibility

System
Pros
Cons

Bare Metal
Maximum performance and consistencyHigh IOPS for intensive workloadsHardware customization and controlBetter security and isolation
Higher upfront costsManual server managementLimited flexibility for scaling

Virtual Machines
Rapid deployment and scalingPay only for resources usedHardware abstraction and flexibility
“Noisy neighbour” performance issuesLimited performance and IOPSHardware abstraction overhead

To slash costs, both companies planned and executed systematic migrations from AWS onto the bare metal servers in colocation facilities and both companies have direct access to hardware instead of virtualized instances.

🚴 OneUptime’s Phased Migration

Prerender chose to use JavaScript everywhere to build expertise in solving the issues caused by JavaScript rendering. They also took advantage of CloudFlare’s distributed system for fast response and global scalability, while their uptime guarantees were supported by Digital Ocean’s cloud platform.

Set up and tested MicroK8s Kubernetes on bare metal for a subset of trafficGradually shifted more workloads onto new servers over weeksCarefully monitored performance and made adjustmentsOnce stable, routed all traffic to the bare metal clusterReduced annual costs by 55%+ (saving over $230,000 per year)🪜Prerender’s Step-by-Step ProcessProvisioned bare metal servers and benchmarked performanceMoved caching and storage services (S3) from AWS servicesSystematically redirected traffic and shut down AWS resourcesContinually stress-tested environment for robustnessCut monthly infrastructure costs by 80% (saving $800K+ per year)

Technologies like Kubernetes, Docker, Docker and Helm enabled frictionless migrations off AWS by providing:

Kubernetes – automated container deployment & scalingDocker – portable, containerized applicationsHelm – simplified Kubernetes application packaging

Together, these enabled flexibility beyond the public cloud vendors.

🛠️ So, What Are the Tradeoffs of Moving Away from the AWS or Public Cloud?

Managing your bare metal servers requires significant investments in skilled staff compared to leveraging AWS’s services. Transitioning to bare metal means you now need specialized DevOps engineers to handle it.

Server provisioning, setup, and managementNetwork architecture and storage configurationContainerization and orchestrationMigration execution and testingSecurity hardening and access controls

This likely requires adding several additional experienced engineers with skillsets like Kubernetes, Docker, database admin, storage/SAN management etc.

At average salaries of $120-160K per engineer, those human resource costs add up rapidly. Plus server hardware expenses. Plus the loss of integrated AWS services like S3, Cognito, and many others accelerated development.

So you need to hire more backend developers to replicate all that useful functionality you’re losing from the cloud provider. Adding 4-6 or more engineers can mean $500K+ in additional annual developer salaries.

In total, you might be spending an extra $2M+ per year in human resources to match what AWS provides out of the box. That’s in addition to physical server costs in the range of $200K+.

So while bare metal hosting fees are lower, the human labor tradeoff is real. The efficiency and integrated services of the public cloud should not be discounted when comparing the total cost of ownership. Relying on AWS or any public cloud means less specialized in-house staff is needed. The male labour costs can rapidly diminish potential bare metal shavings.

✴️ Takeaway — Re-evaluate Public Cloud Costs

For fast-scaling startups, public clouds can end up being far more expensive than bare metal hosting in the long term. The journeys of Prerender and OneUptime highlight how companies can realize major cost savings by moving off AWS onto their bare metal servers.

Their systematic and careful transitions to owned bare metal servers enabled over 50% expense reductions without performance sacrifices or major client impacts. This showcases that while AWS provides power and flexibility, it doesn’t always fit every business’ needs or long-term budgets. Their experiences prove that companies should continually reassess if public cloud services align with their changing business requirements over time.

In the last I want to ask you, Does managing your own infrastructure make sense from a technical and financial perspective? Why or why not?

Based on the Following Resources:

How moving from AWS to Bare-Metal saved us $230,000 /yrHow we reduced our annual server costs by 80% — from $1M to $200k — by moving away from AWSShocking Cloud Costs: How AWS Charged $800,000 for Egress Data Transfer in a Single Month

The benefits, risks, and considerations associated with using open-source LLMs, as well as the comparison with proprietary models.

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Large language models (LLMs) powered by artificial intelligence are gaining immense popularity, with over 325,000 models available on Hugging Face. As more models emerge, a key question is whether to use proprietary or open-source LLMs.

What are LLMs and How Do They Differ?LLMs leverage deep learning and massive datasets to generate human-like textProprietary LLMs are owned and controlled by a companyOpen-source LLMs are freely accessible for anyone to use and modifyProprietary models currently tend to be much larger in terms of parametersHowever, size isn’t everything – smaller open-source models are rapidly catching upCommunity contributions empower the evolution of open-source LLMs

Benefits of Open Source LLMsTransparency – Better visibility into model architecture, training data, output generationCustomization through fine-tuning custom datasets for specific use casesCommunity contributions across diverse perspectives enable experimentationUse Cases

Open-source LLMs are being deployed across industries:

HealthcareDiagnostic assistanceTreatment optimizationFinanceApplications like FinGPT for financial analysisScienceModels like NASA’s trained on geospatial dataLeading Models on Hugging Face

The Hugging Face model leaderboard’s latest benchmarks.

Top LLMs on Hugging Face

Downside of Open-source LLMs

Despite advances, LLMs have concerning have 3 major limitations:

Inaccuracy – Hallucinations from inaccurate/incomplete training dataSecurity – Potential exposure of private data in outputsBias – Embedding biases that skew outputs

Mitigating these risks in early-stage LLMs remains vital.

The Bottom Line

Open-source big language models make AI more available to everyone. This widens who can use them. But risks are still there. Even so, putting information out in the open and letting users adjust models to their needs gives power to people across fields.