The reign of Raspberry Pis as a home server has ended. The majority of hobbyists enjoy using Pis to self-host apps, IoT projects, host media libraries, develop, and run network utilities. That charm still exists, but it’s high time to stop pushing Pi’s limits for home server tasks. Its hardware limitations get exposed when you try to juggle a bunch of projects simultaneously.
I jumped into home labbing with a Raspberry Pi 4B and realized it was a misfit in less than a week. If running a Jellyfin server and Home Assistant wasn’t enoughI experimented with Docker containers on it. Realizing the platform and hardware limitations of the Raspberry Pi, I grabbed a mini PC to function as a home server. That’s when I regretted putting my Raspberry Pi through hell and realized it wasn’t meant to run home server tasks.
I regret working on these 4 Raspberry Pi projects
Table of Contents
The Raspberry Pi family lies at the heart of many DIY projects, some more unsuccessful than others
Lacks enough raw horsepower
Undoubtedly, a Raspberry Pi is capable of hosting multimedia libraries and streaming your content. That’s how most people start, especially the folks who don’t pick it up for IoT projects or development. That said, Pi struggles to transcode 4K videos in real time. Whether you use PlexEmby, or Jellyfin, the CPU and RAM will choke miserably, leaving you with a video stream that stutters and buffers endlessly.
The ARM processor doesn’t pack enough CPU processing power and the integrated GPU muscle to offer hardware-accelerated transcoding. Even if you buy the top-tier Pi 5 with 16GB of RAM, it will only handle hardware decoding of up to 4K videos since it lacks a hardware encoder. To host a media server on capable hardware, you’d need a machine with at least Intel Quick Sync Video support to handle 4K transcodes easily.
High-throughput Network Attached Storage
Can’t handle fast, reliable transfers
Hosting a small media library of songs and photos on a Raspberry Pi works well for limited use. However, it can’t handle high-speed, reliable transfers for Network Attached Storage. You can find YouTube videos demonstrating a Pi-based NAS, but the throughput is nothing to write home about. For starters, the Pi 4 and Pi 5 models offer multiple USB 3.0 ports, but they share the same USB bus.
Even if you use an SSD with Pi, performance is dwarfed by the shared resources between USB ports. Found a PCIe HAT? After that, you can only enjoy PCIe speeds after tinkering around. For a NAS, the Pi’s primary bottleneck is the storage medium. Though it packs a gigabit Ethernet port, it’s not equipped to handle multi-gigabit transfers.
Running apps and services based on Dockers
Container stacks often struggle
Running a couple of lightweight Docker containers as utilities has worked fine for some time. Deploying even a half-dozen containerized apps will quickly max out Pi’s resources. Running Docker Desktop on a Pi is an accident waiting to happen. Even if you manage to run Home Assistant on it, some of its add-ons and flashing firmware using ESPhome make the Pi gasp for resources. Even the 8GB models can struggle to run heavy Docker stacks for development, self-hosting apps, media streaming servers, or simply monitoring your home network.
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Dealing with AI/ML workloads
Even the smallest models crawl
You can deploy local LLM models on a Raspberry Pi, but only the smallest models can run on it. Pi’s integrated GPU isn’t mature enough for model training and clearly lacks GPU acceleration. Even if you get creative and run local LLM models alongside something like Home Assistantthe wait time for responses, despite crisp prompts, makes you reconsider the entire integration. Needless to say, Pi’s CPU and GPU combo aren’t suitable for full-scale AI/ML duties.
Running multiple VMs for different services
Results in a lackluster performance
It is fun to deploy Proxmox on a Raspberry Pibut that’s about it. You can’t extract a smooth performance with multiple virtual machines on a Pi. At most, 2 or 3 VMs can exhaust available memory quickly. Besides, sustained VM tasks can freeze the CPU, and the lack of hardware virtualization makes x86-based VMs crawl. Again, the storage bottlenecks won’t support the constant reads and writes unless you run them off an SSD.
Save the hobby boards from the bottleneck
Raspberry Pis are excellent for IoT projects and running other lightweight services, but aren’t suitable for handling home server workloads. Tasks requiring CPU processing power, GPU muscle, faster storage, and high-throughput network services reveal the Pi’s architectural limitations. Hence, deploying home server functions on a Raspberry Pi results in slow performance and frequent troubleshooting due to instability. Matching resource-intensive workloads with hardware such as x86 mini-PCs or NAS can provide the necessary hardware acceleration. Though the modern Pi is quite capable, it’s not equipped to commission tasks meant for a powerful home server.
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- CPU
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Arm Cortex-A76 (quad-core, 2.4GHz)
- Memory
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Up to 8GB LPDDR4X SDRAM
- Operating System
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Raspberry Pi OS (official)
- Ports
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2× USB 3.0, 2× USB 2.0, Ethernet, 2x micro HDMI, 2× 4-lane MIPI transceivers, PCIe Gen 2.0 interface, USB-C, 40-pin GPIO header
- GPU
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VideoCore VII
The Raspberry Pi is back, and the fifth iteration of the SBC is a lot more capable than the older models. From a new quad-core Arm Cortex-A76 CPU, support for dual monitor setups at 4K 60Hz, and a dedicated power button, there’s a lot to love about this palm-sized computer.
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- Storage
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MicroSD card slot
- CPU
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Arm Cortex-a72 (quad-core, 1.8GHz)
- Memory
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1GB, 2GB, 4GB, or 8GB of LPDDR4
- Operating System
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Raspberry Pi (Official)
- Ports
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2x USB-A 3.0, 2x USB-A 2.0, 40-pin GPIO, 2x micro-HDMI, 2-lane MIPI DSI display port, 2-lane MIPI CSI camera port, 4-pole stereo audio and composite port, microSD card slot, USB-C (for 5V power), Gigabit Ethernet
- GPU
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VideoCore VI
