Don’t pay for multi-gig internet. Fix your LAN instead.


We’ve had multi-gig networking for several years now, but where does it actually make sense? I think that multi-gig internet is a waste of money, as most servers can’t serve you files at those speeds anyway. However, a multi-gig LAN absolutely changed how I use my homelab.

You can only download as fast as a server can upload

You could have 100Gb/s internet, but still only download games at 1Gb/s

Store tab open on Steam. Credit: Jason Fitzpatrick / How-To Geek

I’ve had gigabit internet for nearly seven years now, and I absolutely love it. AT&T started offering 2.5Gb/s and 5Gb/s network plans at my house a few years ago, and I will say, I was initially tempted. The problem with multi-gig internet, though, is that you still are limited by the server you’re downloading from.

A good friend of mine has 3Gb/s internet at his house. His networking equipment is set up to handle it, too—at least up to 2.5Gb/s. The amount of times he actually gets the full 2.5Gb/s download speed on things is very few and far between, however.

Take Steam, for example. Some Steam servers are multi-gig ready, but most still cap out around 1-1.5Gb/s if you’re lucky. Epic, EA, and other game stores are limited by the same thing. Even for myself with gigabit internet, there are many, many times when I don’t even fully saturate a gigabit connection.

So, you can have multi-gig internet all you want, but you’ll still be limited by the place you’re downloading something from. Loading web pages isn’t any better, as those servers are limited by the same issue and there’s also a point of diminishing returns, where just because you’re downloading a website’s contents faster doesn’t mean there’s much of a perceived difference.

Quiz
8 Questions · Test Your Knowledge

Mesh WiFi networks: history, tech, future
Trivia challenge

From military roots to whole-home coverage — how well do you really know mesh WiFi?

HistoryTechnologyBrandsFuture TechFun Facts

The concept of mesh networking was originally developed for use in which field before it reached consumer homes?

Correct! Mesh networking grew out of military research, particularly DARPA-funded projects aimed at creating self-healing, decentralized communications that could survive partial network destruction. The idea was that if one node went down, traffic would reroute automatically — a very useful feature on a battlefield.

Not quite. Mesh networking has its roots in military and DARPA-funded research, designed to create resilient, self-healing communications networks for battlefield use. The decentralized nature meant no single point of failure — a concept that later translated beautifully to home WiFi coverage.

What is the primary technical difference between a traditional WiFi extender and a true mesh WiFi system?

Spot on! True mesh systems use a dedicated backhaul — often a separate radio band — exclusively for node-to-node communication. This keeps the bandwidth used by your devices separate from the bandwidth used to pass data between nodes, resulting in far less congestion and much better performance than a traditional extender.

Not quite. The key differentiator is that true mesh systems use a dedicated backhaul channel between nodes, keeping device traffic and inter-node traffic separate. Traditional extenders reuse the same band for both, effectively halving available bandwidth — which is why they often disappoint in practice.

Which company is widely credited with popularizing consumer mesh WiFi when it launched its first product in 2015?

Correct! Eero launched in 2015 as one of the first consumer-focused mesh WiFi systems and essentially kicked off the home mesh revolution. Its simple app-based setup and attractive hardware stood out in a market dominated by ugly router boxes covered in antennas. Amazon later acquired Eero in 2019.

Not quite — Eero gets the credit here. Founded in 2014 and launched to consumers in 2015, Eero was a pioneer in making mesh WiFi accessible and appealing to everyday users. Its clean design and smartphone-based setup felt revolutionary compared to traditional router management interfaces.

A mesh WiFi network behaves similarly to which surprisingly ancient human communication system?

Great analogy — and you got it! Mesh networking mimics the way gossip spreads: each node receives information and passes it along to the nearest neighbor, with multiple paths available if one route is blocked. Computer scientists actually call one mesh routing method ‘gossip protocol’ for exactly this reason.

Fun guess, but the best analogy is gossip spreading through a village. In mesh networking, data hops from node to node along the best available path — just like a rumor finding its way through a crowd. Computer scientists even formally named one routing approach ‘gossip protocol’ in honor of this similarity.

WiFi 6E and WiFi 7 mesh systems introduced support for which frequency band that older mesh hardware cannot use?

Correct! WiFi 6E opened up the 6 GHz band for consumer use, giving mesh systems a much less congested slice of spectrum to use — especially valuable as a clean, fast backhaul channel. WiFi 7 expands on this further with multi-link operation, letting devices use multiple bands simultaneously.

The answer is 6 GHz. WiFi 6E was a significant leap because it unlocked the 6 GHz band — a largely empty, high-capacity range of spectrum that dramatically reduces interference, especially in apartment buildings packed with competing networks. Mesh systems use it as a super-clean backhaul highway.

Before dedicated mesh systems existed, some creative users built their own mesh-like home networks using open-source firmware called what?

Well done! DD-WRT was the go-to open-source router firmware for enthusiasts who wanted to squeeze extra performance and features out of consumer routers — including running multiple routers in coordinated configurations that resembled mesh behavior. It’s still actively developed today and has a devoted following.

Not quite — the answer is DD-WRT. This legendary open-source firmware let tech-savvy users replace the factory software on routers from brands like Linksys and Netgear, unlocking advanced features including multi-router setups that approximated mesh networking years before polished consumer mesh products existed.

Which emerging concept would take mesh networking beyond the home and create a massive, self-organizing internet built from billions of everyday devices?

Exactly right! The Internet of Things vision includes smart devices — thermostats, lights, sensors, appliances — forming spontaneous mesh networks with each other, passing data along without relying on a central router or ISP infrastructure. Standards like Thread and Matter are already pushing this concept into real homes today.

The answer is the IoT mesh. The Internet of Things roadmap envisions billions of smart devices forming organic, self-organizing mesh networks — communicating peer-to-peer without needing a traditional router as a middleman. Protocols like Thread (used in Matter-compatible smart home devices) are making this a reality right now.

What quirky real-world project demonstrated mesh networking by connecting an entire island community with a DIY WiFi mesh built mostly from recycled hardware?

Correct! Guifi.net, launched in rural Catalonia in the early 2000s, grew into one of the world’s largest community-owned mesh networks with tens of thousands of nodes. It was built by volunteers using cheap or recycled hardware to bring internet access to areas ignored by commercial ISPs — a remarkable grassroots achievement still operating today.

The answer is Guifi.net. This incredible volunteer-built mesh network in Catalonia, Spain, started in the early 2000s and eventually grew to over 35,000 active nodes, making it one of the largest community mesh networks on the planet. It proved that determined communities could build their own internet infrastructure without relying on big telecoms.

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Multi-gig networking is more than just a faster outside connection

I’m all about that LAN

While a faster external connection might not make a lot of sense, having a multi-gig LAN definitely does. Just over a year ago, I upgraded my own local network from gigabit to 2.5Gb/s and it changed everything for me.

While an external server, like Steam, might limit you with a multi-gig connection, you can actually build your own internal systems to fully saturate just about any networking standard you choose to use. You see, chances are, those Steam servers could serve files at multi-gig speeds, but their own internet connection is likely the limiting factor.

Since you’re the one building your own networking infrastructure, it’s very easy to make sure that every device in the network has multi-gig capabilities. For me, that means having a multi-gig switch and network cards in all necessary devices. SSDs definitely can hit multi-gig speeds, and most RAID arrays are also capable of that type of speed for file transfers.

Building out a multi-gig network is about way more than just a faster outside connection—it’s all about the internal connection your systems have. Plus, by setting up a multi-gig local network first, you’re preparing yourself for the inevitable upgrade to a multi-gig internet connection.

Faster NAS access is life-changing

If your life revolves around computers, at least

USB, Ethernet, HDMI, and OCULINK ports on the UGREEN iDX6011 Pro NAS. Credit: Patrick Campanale / How-To Geek

So, how did a multi-gig local network change everything for me? Well, it removed some bottlenecks that I was experiencing in my own homelab. For example, on standard gigabit, file transfers from my desktop or laptop to my NAS would take forever. Sometimes I would actually avoid doing a network file transfer because of how slow it would be. Instead, I’d just grab an external SSD or flash drive and move the files to that because it was faster.

This became a problem, because I wasn’t actually using my NAS as a NAS—it simply was just a Plex server and nothing more. That’s fine and all, but I deal with a lot of data and really need to keep a lot of it backed up, which is something I simply wasn’t doing.

Moving my network from gigabit to 2.5Gb/s meant I could transfer data at 2.5x the speed that I used to. That might not sound like a ton, but a file transfer that used to take 10 minutes now takes just 4. A one hour transfer takes just 24 minutes now.

By going multi-gig on my LAN, I also opened up my NAS to more than just archive file storage. Now, I store active video and photo projects on it and work directly from my NAS on my computer. This allows me to keep my laptop’s storage optimized and keep the big working files on the NAS where they belong.

Another benefit to a multi-gig LAN is that the servers can talk to each other faster than they could before. I have my movie and TV shows stored on my big 12-bay rack-mount server, but the system that actually runs Plex is another computer that has way more processing and transcoding power. That means my Plex media is being read over the network, and having the extra bandwidth of a multi-gig connection ensures I won’t have any stuttering or buffering issues with the media.

  • The Unifi Dream Router 7.

    Brand

    Unifi

    Range

    1,750 square feet

    The Unifi Dream Router 7 is a full-fledged network appliance offering NVR capabilities, fully managed switching,a built-in firewall, VLANs, and more. With four 2.5G Ethernet ports (one with PoE+) and a 10G SFP+ port, the Unifi Dream Router 7 also features dual WAN capabilities should you have two ISP connections. It includes a 64GB microSD card for IP camera storage, but can be upgraded for more storage if needed. With Wi-Fi 7, you’ll be able to reach up to a theoretical 5.7 Gbps network speed when using the 10G SFP+ port, or 2.5 Gbps when using Ethernet. 


  • Ugreen iDX 6011 Pro AI NAS.

    Brand

    UGREEN

    CPU

    Intel Core Ultra 7 255H

    The Ugreen iDX 6011 Pro AI NAS is one of the most powerful NAS servers in the Ugreen lineup. With Intel’s Core Ultra 7 255H 16-core processor and 64GB of LPDDR5/x RAM onboard, there’s more than enough power to handle anything you can throw at this system. Add to that dual Thunderbolt 4 ports, dual 10GbE LAN ports, an OCuLink expansion port, and more, and you have a very solid network attached storage system.



A multi-gig network just makes your homelab easier to use

At the end of the day, a multi-gig network simplifies so much in a homelab. It makes file transfers faster, which makes it feel more seamless. It allows servers to communicate at faster rates, which can help reduce buffering or backup times.

However, a multi-gig internet connection doesn’t really improve a whole lot. Sure, it helps make a few things faster to download, but, it’s not nearly as beneficial as it might sound. I just don’t see the need to pay the extra for a multi-gig internet connection right now. A few years from now it might make sense, but, in 2026, there’s not enough multi-gig servers serving up files for multi-gig internet to make a ton of sense.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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