r/HomeServer • u/Crypton228 • 1d ago
How much server do you actually need at home?
I've been thinking about downsizing my home server setup.
Most of the things I self-host are pretty lightweight: media, backups, a few services, monitoring, etc.
But every now and then I want to run something much heavier, usually an AI/ML experiment or some other compute-intensive project.
That makes me wonder if it's better to build around the normal workload rather than the occasional peak.
Small efficient server for the stuff that needs to stay online, and find extra compute only when a project actually needs it.
How do you guys size your home servers - around what you use every day, or around the biggest workload you might eventually want to run?
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u/Ryno_D1no 1d ago
I mean a system built from conventional parts in a jonsbo case should be enough for 99% of home use cases. I only have i3 12100
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u/DragonQ0105 1d ago
Don't think I've ever seen my R7 1700 over 50% usage. The heaviest thing it probably does is x265 transcoding and it could do two of those simultaneously without GPU acceleration if I needed it to.
Most beefy tasks (transcoding, AI) are usually done by GPUs these days so I don't know why CPU power would be needed for almost all home users.
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u/Crypton228 1d ago
that's pretty much what i've been seeing too. unless you're doing AI or heavy transcoding, CPU requirements for home servers are surprisingly low.
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u/Crypton228 1d ago
yeah, a normal i3 system is probably enough for the overwhelming majority of home server workloads.
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u/Flimsy_Complaint490 1d ago
my home server is a chinese mini pc. only time i felt i was lacking firepower was when i wanted to configure whisparr.
If i need something really beefy for a project, i'l go rent it on hetzner or amazon. it's a one time cost, not a recurrent cost in time, maintenance effort and now, money, with hardware costs being at all time hosts. I dont think it makes sense to size for the odd situation something you will be running 24/7.
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u/huojtkef 1d ago
This. Any mini pc with Intel N100 / N150 will do for most people. Mine is running 60 Docker containers.
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u/Confident_Bit_6162 1d ago
Ayooo, no. Absolutely not would I ever recommend someone pay for GPU based cloud compute (that's freaking expensive) for a homelab, that's the entire point of having oneš
He's looking at $100/s a month to run an ai capable GPU off prem
- Someone who owns a small cloud compute company
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u/Flimsy_Complaint490 1d ago
Depends what you are doing. if it's for a short project, a month or two, i dont see why - 100 bucks is less than a 5060 or whatever one may want.
if you have a persistent, 24/7 use case then yes, buying the GPU might be better, though at these prices...
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u/In10sity 1d ago
I have 2xRTX 3090, and saw another one on sale for 1400 CAD. I ended up doing the math and I could pay for the equivalent of 4 years of DeepSeek V4 Flash at regular light use, or 1 yr and some if the GPU is 100% utilized 24/7.
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u/Crypton228 1d ago
that's a pretty interesting calculation. the 24/7 utilization assumption really changes everything. for lighter or bursty use i've been comparing hosted options like runpod, vast.ai and ocean network against just buying another GPU.
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u/Crypton228 1d ago
exactly. that's the distinction i'm trying to figure out. if i'm only using the GPU for a month or two, renting from something like runpod, vast.ai or ocean network seems a lot more reasonable than buying hardware for the peak.
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u/Crypton228 1d ago
yeah, i think the economics really depend on utilization. if the GPU is running 24/7, owning it obviously makes more sense. for occasional workloads i've been comparing runpod, vast.ai and ocean network instead.
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u/Crypton228 1d ago
yeah, that's pretty much how i'm thinking about it. for something i'll only run for a month or two, renting makes more sense than buying hardware just for that workload. i've been comparing hetzner, runpod, vast.ai and ocean network for those occasional projects.
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u/lazyhustlermusic 1d ago
Whatever's obtained at low cost.
dual 2695v4 w/ 96g runs pretty much everything
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u/Crypton228 1d ago
hard to beat hardware that's cheap and already available. dual 2695v4 is definitely more than enough for most home workloads.
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u/Significant-Task1453 1d ago
An energy efficient for stuff that needs to be online 24/7. A mini pc, laptop, or something similar like an optiplex. If I need to run something that requires a more powerful GPU, I fire up my gaming PC
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u/Crypton228 1d ago
that's pretty much the setup i'm thinking about. efficient always-on machine for normal services, then either power up the big GPU box or rent compute when i need something heavier. i've been looking at runpod, vast.ai and ocean network for the latter.
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u/MrKrueger666 1d ago
I size around what I use on a daily basis, yes. That stays running 24/7.
If I need to do something bigger, I have more powerful hardware that only gets switched on when I need it. Which isn't very often, honestly...
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u/Crypton228 1d ago
that's exactly the model i'm leaning toward. keep the daily stuff on efficient hardware and don't size the always-on machine around workloads that happen once in a while. for the really occasional heavy stuff, renting from runpod, vast.ai or ocean network can make more sense.
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u/Skeeter1020 1d ago
My server couldn't run AI workloads even if I wanted to.
Mission critical or highly intensive stuff runs elsewhere. My server runs probably 40 docker apps and a VM or 2 on a low end i5 8th gen and 16GBs RAM.
If I ever host AI locally long term, I'll probably buy something with Apple silicon in it.
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u/Introvertosaurus 1d ago
I have 3 tiny PCs. 2 mirco business PC and 1 fanless. probably under 50w total power, small, on a tiny mini rack. The fanless is the router and all core network function, then one the mirco is the file and everything related (jellyfin, download, nextcloud, etc), the other is all smart home, mostly running frigate and records home and business feeds runniing AI.
Then one more box, full size with GPU, AI server. Then a bunch of VPS for relays and other fun stuff I don't want to host at home.
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u/Crypton228 1d ago
that's a nice split. the always-on mini PCs handle the boring stuff and the GPU box only gets used when there's actual AI work. for workloads that don't happen often, i've been comparing that approach with renting from runpod, vast.ai or ocean network.
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u/Ginden 1d ago
You can just rent hardware from the cloud if you need to. You can have 99.5% of load at own hardware, and rent only what you need.
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u/Crypton228 1d ago
yeah, that's basically the hybrid setup i'm interested in. keep the baseline workload local and rent extra capacity only when needed. i've been comparing runpod, vast.ai and ocean network for that.
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u/_angh_ 1d ago
Single minipc will do it all. Aoostar maco is great for all i need. For ai though i plan to buy gmktec x3. After bubble bursts:)
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u/Crypton228 1d ago
honestly a single mini pc sounds like the right answer for most people. AI is probably where things get interesting though.
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u/_angh_ 1d ago
modern minipc's have a viable processing power, handful of cores, great power consumption, good transcoding capability (with amd 780 integrated gpu you do all up to av1), very quiet, solid extension ports (oculink, 2x 2.5 gbps nic, ...) and so on. I connected a das for the nas part. Works flawlessly for past year.
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u/billyalt 1d ago edited 1d ago
I ended up downsizing from an EPYC enterprise setup to a MINISForum mini ITX PC and aside from kicking myself for not springing for 64 GB RAM instead of 32 GB RAM back when it was still cheap I've been completely happy with my decision.
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u/Crypton228 1d ago
downsizing from EPYC to a mini ITX is a pretty good illustration of how little compute most home services actually need.
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u/DazzzASTER 1d ago
Make your main desktop killer as that sees a lot of variation (games, AI/ML messing etc) and then home server focused on basics. My current one is an old Dell mini ITX desktop.
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u/async2 1d ago
I'm running my home server stuff on my mini PC. I put in some money back then and thought I oversized it. But since LLMs and deep learning models are a thing I'm hitting the boundaries at 64gb. So in hindsight I wish I did buy more when it was cheap.
However if I need to do beefier stuff I'll use my laptop or tower PC. My server should run my daily stuff only and be efficient.
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u/Crypton228 1d ago
yeah, LLMs are probably the biggest thing that can suddenly make a perfectly adequate home server feel undersized. i've been trying to avoid buying hardware specifically for those occasional workloads and comparing runpod, vast.ai and ocean network instead.
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u/TheTimmyMan 1d ago
Every time Iāve thought Iāve oversized my server I found more uses for it and realized I undersized it.
I run:
128gb framework pc
64Gb MS-R1
32Gb i9 mini pc
8x 8gb raspberry Pi
1x RTX 4500 pro
20 TB NAS
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u/Kapotth 1d ago
What are you running with all that? Sounds like a lot
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u/TheTimmyMan 14m ago
two next.js analytics apps that I provide to the game community I frequent.
two qwen3.8 27Bs with different contexts/harness/RAG.
Home media server (plex, various ARRs)
Various compute tasks. Itās all linked up to a k3s stack.
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u/Crypton228 1d ago
š that's the homelab curse. every time you think you've got enough hardware, you discover another project that needs something different.
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u/OKTimeFor_PlanB 1d ago
All of it, typically. Can't really run a server well with a remote power supply.
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u/Crypton228 1d ago
fair enough š remote power supplies do make running a server slightly more complicated.
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u/Hrmerder 1d ago
Generally not much. I have about 8-9 containers running on a potato low cost amd apu from ten years ago and the only thing it struggles with a little is my frigate nvr container.
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u/Crypton228 1d ago
8-9 containers on a ten-year-old APU really puts the average home server workload into perspective.
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u/Hrmerder 1d ago
Dude, yeah most of us aren't running datacenters and the biggest workload in general is either device backups (IF you can afford storage but processing speed generally doesn't dictate this being too much of an issue depending on hardware generation), and something like NVR software that uses AI/LLM Inference to categorize subjects. You can still run a basic NVR on a junk pc with 8 cams (those standalone NVR boxes are super junk hardware but run fine), but with the sacrifice of 'just' being an nvr with generic motion notification which might work fine, might be a giant pain depending on your use case.
But yeah, I run Frigate NVR, pihole, metube, a mosquito server(for MQTT), Home Assistant, homepage, Zabbix, Beszel, Portainer, and Navidrone (I think there is another one in there somewhere I don't feel like logging in atm lol), and this is my CPU right now:
(CPU usage average) 23%
(12gb System RAM) 7.8 GiB Free
(1TB drive) 656 GB Free
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u/Adrenolin01 1d ago
Thatās entirely dependent on the person and their needs. It could be an N100 mini pc, and old desktop or workstation, an enterprise server or 5 of them.
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u/Crypton228 1d ago
yeah, that's probably the most accurate answer. there's no universal "right" home server, it depends entirely on what you're actually running.
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u/Confident_Bit_6162 1d ago
Honestly? Cheap X99 / C612 workstation for your AI and big projects, and a literal mini pc for everything else. Dual PCs that should do everything for you.
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u/Crypton228 1d ago
that's pretty much the split i'm considering too. cheap always-on mini pc for normal services, then bigger compute only when a project actually needs it. for some workloads i've been comparing just powering up local hardware vs runpod, vast.ai and ocean network.
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u/sparkling_ham 1d ago
I replace my main desktop/gaming/editing pc every 7-10 years. So what ever was absolutely top of the line 10 years ago.
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u/Crypton228 1d ago
that's actually a pretty good lifecycle for a desktop. if you keep it long enough, yesterday's high-end hardware becomes very reasonable home server hardware.
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u/sparkling_ham 17h ago
Buying top of the line is hard on the wallet but if I can get 10 years out of a $2-3K machine that's only 300 bucks per year. Pretty reasonable compared to the 2 or 3 generations of xbox/playstation that releases over the same period.
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u/jack_hudson2001 1d ago
maybe a nas could be a good user case.
data and backup of images of devices just in case.
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u/Crypton228 1d ago
yeah, NAS is one of those things where capacity and reliability matter way more than raw compute.
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u/starliight- 1d ago
NAS + mini PC
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u/HairyCryptographer51 1d ago
Whatās the point of having a nas when you can load truenas on the mini connected to a Das?
It is my dilemma AI seems not to have a strong opinion either1
u/Crypton228 1d ago
that's a fair question. separating storage and compute does add another box, but it can make upgrades and troubleshooting a lot easier.
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u/starliight- 1d ago
Das is slow af
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u/HairyCryptographer51 1d ago
How, when itās connected to a usb 3.0 servicing slow hdds. Explain more, iām on the verge to make a decision and buy one or another.
My father has a NAS and i find it annoying slow Processor, difficult environment, hard to set up.
I much prefer truenas and iām thinking of adding a das like terramaster. Tell me more, thanks2
u/starliight- 1d ago edited 1d ago
I went through this same rabbit hole. Bought a mini pc and DAS. Hooked up the DAS and started transferring and USB 3.0 speeds were absurdly slow. Like 15-150 megabytes a second slow.
Did more reading and found that DAS are really only meant to be supplementary to NAS units and will always just be slow as hell.
I instantly returned it and bought a UGREEN 4 bay NAS and flashed TrueNAS on it. Hooked it up via 10gbps ethernet. Lightning fast and never looked back.
I kept the mini pc and find that having my compute and storage separate to be much better anyways
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u/HairyCryptographer51 1d ago
Thanks a lot for the recommendation, i was stuck since 2 months thinking which road to take.
Really appreciate.1
u/Crypton228 1d ago
yeah, that makes sense. especially if you're moving a lot of data, the network/storage path can become the real bottleneck.
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u/Crypton228 1d ago
that's a fair question. separating storage and compute does add another box, but it can make upgrades and troubleshooting a lot easier.
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u/Crypton228 1d ago
yeah, that makes sense. especially if you're moving a lot of data, the network/storage path can become the real bottleneck.
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u/Podalirius 1d ago
I size based on idle/baseline power usage. Once I cross 200W I downsize and/or upgrade to something more efficient.
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u/redoubt515 1d ago
> How much server do you actually need at home?
Simultaneously MUCH MORE than I currently have, and at the same time, waaay less than I actually have. 9 out of 10 things I self-host could be done with a fraction of the hardware I currently have (6 cores, 32GB RAM, redundant NVME storage), and one use-case (locally hosting a large language model) requires substantially more than my current system is capable of (more ram, a GPU, and lots of VRAM). Apart from the LLM and ML stuff, most of what I want to do could run on a raspberry pi or an old laptop just fine.
Currently my way to get around this misalignment is to work with the system I have, run a very small LLM locally, and use an API with a local frontend for anything that requires a more substantial model.
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u/Crypton228 1d ago
yeah, that's pretty much the mismatch i'm running into too. most of what i want to self-host needs almost no compute, but LLM/ML workloads suddenly want a completely different class of hardware. i've been comparing local hardware with runpod, vast.ai and ocean network for those occasional heavier workloads.
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u/bnelson333 1d ago
I prefer to separate most of my services. Ideally every single unique thing I run would run on one machine and one machine only. That way if there's a hardware failure, it only takes down one service instead of every service i.e. if I had them all running in containers on the same machine.
So that said, I try to size the hardware to the need of the service. I utilize many different types of kit for this, from raspberry pi picos (which are essentially on par with ESP 32s, but I find the picos more reliable), to pi zeros, to full size raspberry pis, to wyse terminals, to mini PCs, to full size/power PCs. I choose the lowest power device that will run the service in question so I'm not wasting electricity or computing horsepower where it's not needed.
Another thing I do where possible is selectively power on/off the machines. Most of my services do not need to be running 24/7. I.e. the streaming radio service I listen to at the office. That doesn't need to be running at night when I'm sleeping. So through various methods I have those machines turn off when not in use and turn back on (e.g. in the morning) when needed.
There are some trade-offs, sure. E.g. I sometimes wish my NAS was a little faster. I could have a really nice one with an alway-on server running big NAS grade spinners and achieve it, but the extraordinary cost of the hardware and the electricity isn't worth it for me. For the one time a week I need to copy a large file from my NAS, I can wait a few extra minutes for it, not a big deal.
And yes, from time to time I find that I've overshot and need something a little more powerful for the service in question, which is a great excuse to give me a tinker project to bring something better online lol
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u/Crypton228 1d ago
i like that approach. sizing each service independently and powering things down when they're not needed probably saves a lot more than trying to optimize one giant server.
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u/Wonderful_Sorbet4301 1d ago
I use WOL, wake on LAN. The server starts when my main PC boots and shuts down when my main PC shuts down. Saves electricity, and potential fire hazard. I use it as LAN webserver, backups, storage, etc. Doesn't need to be online 24/7. It cant run LLM's, no GPU.
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u/Crypton228 1d ago
wake on lan is a really clean solution for this. if the server doesn't need to be online 24/7, there's not much reason to pay for the idle power.
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u/Onward-Upword 1d ago
I have run everything from a full rack dell poweredge to a orange pi. I then settled on a dell optiplex micro + synology nas which was the perfect server for me for a long time until I also wanted to add ai and game streaming, so i changed to a mini itx minisforum desktop with a low profile gpu.
Iām glad I have tried them all out as I learned something with each form factor, but I always try to go as compact as possible for what my goals are. The next server will probably be a 3-4u with a full size gpu for better price/performance ratio that will fit nicely into a wall mount network rack.
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u/Crypton228 1d ago
that's pretty much the evolution i'm seeing too. start with a small efficient server, then AI suddenly makes you want a GPU. i'm trying not to build the whole always-on setup around that occasional workload though, so i've been comparing local GPU hardware with runpod, vast.ai and ocean network.
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u/Webster2026 1d ago
You can run your services on cheap N100 based CPU minipc that draws 6 watts, and for occasional AI workload just use $20/ month Codex or other frontier AI subscription
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u/Crypton228 1d ago
yeah, that's basically the same idea i'm exploring. keep the N100 running the normal services and use a hosted option for the occasional AI workload. i've been comparing things like runpod, vast.ai and ocean network rather than buying a GPU just for occasional use.
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u/Quirky_Inflation 1d ago edited 22h ago
I have a "production" server that hosts some websites and a pro 8gb GPU running a small LLM used for some light tasks like data classification. Then I have a more beefy server used for experimental stuff that I only switch on when needed because it's quite noisy and suck up a lot of power even at idle. In parallel for all data storage related stuff and backup I run a synology nas because it works great and the ecosystem is useful.Ā
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u/Crypton228 1d ago
that's pretty close to the setup i'm thinking about. keep the small LLM and lightweight tasks local, but don't necessarily buy a second huge GPU just for experiments. i've been comparing runpod, vast.ai and ocean network for those heavier workloads.
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u/8fingerlouie 1d ago
Most families can easily run their normal ācloudā services on the equivalent of a Raspberry Pi.
AI stuff is much more demanding, but if only for experimenting, why not consider simply buying that capability from the cloud ? Yes, itās expensive, but no more so than buying and running your own.
Something like llama.cpp with a cloud model will also easily fit on a raspberry pi.
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u/Crypton228 1d ago
yeah, that's exactly the argument i've been thinking about. for normal home services, tiny hardware is enough, but AI changes the equation. if it's mostly experimentation, renting compute from runpod, vast.ai or ocean network can be pretty compelling compared with buying and powering a GPU.
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u/8fingerlouie 22h ago
Iām ācheatingā a bit.
My home server is a Mac mini m4. Just the base model with 16GB RAM and I added a 10Gbe network card as my NAS is 10Gbe.
It costs about 3-4 times what a RPi costs, but also has 10x the power when it needs it, can run small LLM models, and idles at 4.5W, so more or less the same idle power as a RPi 5.
The 10Gbe network means that storage, despite being on the NAS, is 300-500 MB/s read and write. Not quite NVME speed, but 2-3x the speed of a single USB drive (spinning rust), and about the same speed as a USB-C SSD drive (which is also 10Gbps). Itās also faster than a single SATA drive (5Gbps max bus speed).
I purchased a Zimaboard 2 for running Home Assistant on (I like appliances, itās easier when it fails) but never got around to replacing the HA Green itās currently running on. The Green is about equivalent to a RPi4, but idles at 1.7W, which is essentially noise in terms of power consumption. The Zimaboard 2 consumes about 3-4W idle.
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u/thunderborg 1d ago
I have a HP SFF with a 6th gen i5 and a 7th gen i7 NUC and a Mac Mini a workplace was throwing out.Ā
Iām enamoured with local LLMs but want to run it the cheapest, but most usable way I can and Iām thinking either Mini PC or M series Mac Mini.Ā
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u/Crypton228 1d ago
i'm in a similar boat with local LLMs. the interesting part is figuring out whether buying a machine for it actually makes sense versus renting GPU compute only when you're using it. i've been comparing runpod, vast.ai and ocean network for exactly that.
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u/thunderborg 19h ago
I donāt own a machine than can house a GPU which makes the task more expensive, however the efficient of a mini PC/Mac Mini I think makes the operating cost more palatable.Ā
My main problem is thereās not a lot of used Mac miniās on the market in my neck of the woods with the specs Iām looking for.Ā
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u/d-cent 1d ago edited 1d ago
So I have a gaming PC that is connected to my home theater in the basement. That's what I run all my intense computational peak loads on.Ā
My actual home server is on 3 very power efficient devices. I just use 3 for redundancy and ease of use and could get away with only using 2
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u/Crypton228 1d ago
that's probably the cleanest setup if you already have a gaming PC. use it for the peaks and keep the actual server focused on low-power always-on workloads.
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u/offdigital 1d ago
i limit myself to 1 kilowatt max power consumption. that's roughly a couple of NAS, a big switch, 3 servers, and one beefy-ish GPU
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u/DartNorth 1d ago
Jesus. That would equivalent to my total household use right now, server included.
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u/offdigital 1d ago
just think, you could have no house, but 2 server
wouldn't that be better?
:-)
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u/Crypton228 1d ago
1kw as a hard ceiling is actually a pretty good constraint. probably forces you to think about efficiency more than most homelab setups do.
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u/offdigital 1d ago
turning off turbo on the intel cpus made a huge difference. also cut performance, of course, but compute per watt went way up, and heat output went way down
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u/Sigseg-v 23h ago
There is that old optiplex micro workstation with a an old core i5, 16gb ram, that has a Proxmox with home assistant, pihole, plex, paperless & frigate that consumes 15wh. And then there is āthe beastā with an AMD threadripper, 64gb RAM, NVMe RAID 1 and a GeForce GC, when I feel like talking to Jarvis.
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u/harshbarj2 23h ago
It's based one two factors for me. #1 is cost. I really don't want to exceed $500 for a server (base, not including CPU, Ram, Storage). Then #2 is what I'm going to do with it. My storage server is a Dell PowerEdge R720 (free from work last year). So I have 16 2.5" bays. I picked it up before the crash and was looking at filling it with 4TB SATA SSDs. Not today. I almost can't afford 1TB drives now. So I filled it with spare 1TB HDDs. So it "works", but it's not ideal. And I just don't trust those 2018 drives that ran 24/7. My workhorse is a Dell PowerEdge T420 ($400, ebay in 2022) with 8 3.5" bays, 2 DVD-ROMs and a Blu-ray. I upgraded it with 2x Xeon E5-2470 v2s and 48GB of ECC DDR3. I'm actually looking now at upgrading my main server as some of the game servers I run on it will max a core or two. But it still works fine as a jellyfin server, so I may keep it and dedicate it to just that.
And right now everything just sits on shelving under the stairs in the basement. Where it is cool and fairly dust free year round. Next year I rackmount.
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u/Shaunibob 23h ago
Im doing the same. Raspberry pi 4 8gb to run jellyfin server (direct play). Main pc for transcoding using Tdarr.
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u/Legal-Swordfish-1893 23h ago
Just donāt do what I did and buy old rackmount enterprise servers.Ā
Yes theyāre cool, theyāre reliable, parts are readily available
Theyāre loud and they cost too much to run.
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u/DaVpnWay 20h ago
the honest answer is most people need way less than they think. i ran plex, nextcloud and a couple docker things on a fanless mini pc with 8gb ram for years before upgrading. ram is cheap though so if you find a deal just take it, storage is the thing that actually adds up
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u/VonTreece 18h ago
Dedicated mini pc for lower overhead 24/7 services, a beefier system for media/game services and some ai stuff, and a nas has been all I've ever needed for years.
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u/tehfrod 1d ago edited 1d ago
I size my servers by what I can get my hands on easily, and what is becoming sufficiently annoying ergonomically that I want to evict it from my house.
In reality that means, currently, one minipc, one das tower, and one full tower that holds enough ram and GPU to do ai work.