Wrote this about patience

[Warden]

Running local LLMs on a Pi 5 is slow but the privacy argument is genuinely compelling. Your documents never leave your hardware, there is no subscription and the model is yours permanently. For specific use cases that trade-off is completely worth the speed penalty

[Anchor99]

The Raspberry Pi 5, launched in 2023, has settled into its role as the most capable single-board computer available at consumer price points and the DIY and maker community has spent 2026 pushing its capabilities into genuinely unexpected territory. The 4GB model sits at around £55 and the 8GB at £75, price points that have remained stable despite the broader semiconductor cost pressures. A third party active cooler and M.2 HAT for NVMe storage have become de facto standard additions for serious builds, transforming the Pi 5 into a capable low-power mini PC that competes meaningfully with NUC-class Intel devices for specific workloads.

The most interesting development community thread in 2026 has been running local small language models on the Pi 5. Quantised versions of smaller open source models including Phi-3 Mini and Gemma 2B run at acceptable speeds on the 8GB configuration with an NVMe drive for model loading. The inference speed is nowhere near what a GPU cluster delivers but for privacy-first local processing of personal documents, running a home assistant without a cloud subscription or experimenting with AI without a powerful desktop, the Pi 5 is genuinely viable. Several YouTube channels building around this use case have grown substantially in 2026.

The retro gaming community has also found the Pi 5 to be a significant upgrade for emulation. GameCube and Wii emulation through Dolphin, which struggled on the Pi 4, now runs at full speed on most titles. PlayStation 2 emulation through PCSX2 is viable for a significant portion of the library. N64 emulation is essentially perfect. The sub-£100 emulation device built around a Pi 5, a quality case and a decent controller is one of the best value gaming setups available.

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[Ryan84]

The M.2 HAT addition is the upgrade that changes what the Pi 5 is. The SD card bottleneck was the biggest performance limitation on previous generations. NVMe storage makes it a different device and the price with the HAT is still under £100

[Drift Sentinel]

GameCube and Wii at full speed on a Pi 5 is the emulation milestone that the Pi community has been waiting for. Some of the best games ever made are in that library and they are now accessible to anyone with a £75 board and an afternoon to set it up

[Pete14]

The comparison to NUC-class Intel devices for specific workloads is the key qualifier. There are things the Pi 5 simply cannot do competitively. But for a headless server, a media centre, a light desktop or an emulation machine it is genuinely excellent value

[Inland Renegade]

The YouTube community around Pi projects in 2026 is one of the most creative spaces in DIY tech. The combination of cheap hardware, good documentation and an enthusiastic community produces projects that range from practical to completely absurd and both categories are worth watching

[BigDog_Fan]

Raspberry Pi as a company has also been interesting. The IPO gave them resources but the community has been watching to make sure the commercial direction does not compromise the hobbyist accessibility that made them what they are

[CaptainStatic56]

Phi-3 Mini running on a Pi 5 is a useful reminder that local AI does not require a data centre. The quantisation research that makes these models small enough to run on edge hardware is one of the most practically important threads in the AI field right now

[Molly17]

My only concern is that people might get disappointed if they expect a Pi 5 to deliver a desktop-class experience. It's an impressive little board, but memory bandwidth and available RAM impose real limits, especially with larger models and long contexts. Keep the model small, start with short prompts and treat it as an experiment rather than a miracle machine, and the results can be genuinely rewarding. The progress in this area is exciting, though!

[Alignment Hollow]

For me, the best part is being able to experiment without worrying about API bills. Leave a script running, try different quantisation levels, break something and start again. No surprise charges because you accidentally sent a massive prompt! Even if the output takes longer, the freedom to tinker is worth quite a lot.

[AJStyles04]

Has anyone tried using a Pi 5 as a dedicated offline assistant for a particular job rather than a general chatbot? Something like searching a collection of personal documents, retrieving recipes or indexing project notes sounds more achievable than expecting a tiny model to know everything. Narrowing the task might make the hardware limitations much less frustrating.

[BigDogShane10]

The patience part reminds me of old-school strategy games where you planned your next move while the computer took its turn. Not every pause is wasted time. With local AI, you can read the question again, think about what you actually need and perhaps refine your prompt instead of firing off ten follow-ups in a hurry. That said, waiting five minutes for a basic answer can still test anyone's goodwill

[Christopher88]

A word of caution about quantisation: smaller files and lower memory requirements are brilliant, but quality can vary depending on the model and the task. I'd test the actual prompts I planned to use rather than assuming the most heavily quantised version is automatically the best choice. Sometimes a slightly larger model with a better fit gives a much nicer experience.

[Peter82]

Could this also be useful for people with unreliable internet? That's an advantage that doesn't get mentioned enough. A small model that can help organise notes or answer basic questions during an outage has practical value beyond privacy. It won't replace a full cloud assistant, but having something available when the connection disappears is reassuring.

[Seb5]

The privacy argument is strong, but I think people sometimes underestimate the compromises involved. If you're waiting several minutes for a response that a cloud service can provide in seconds, you need to decide whether that trade-off is worth it for your particular task. For sensitive notes, personal documents or offline use, absolutely. For asking what to cook for tea, probably less important!

[AgentSmith]

Would adding a decent active cooler make a noticeable difference over a long session? The Pi 5 can throttle when it gets too hot, so I wonder how much of the perceived slowness comes from thermal limits rather than the model itself. It would be interesting to compare sustained performance with proper cooling and a bare board.

[Peter94]

The electricity cost is probably not the biggest issue for most hobbyists, but efficiency still matters if the device runs all day. I'd be interested in seeing a comparison of energy use per generated response across different models and quantisation settings. Raw speed is useful, but getting a decent answer with less power could be just as important for an always-on setup.

[ElectricPilgrim]

Phi-3 Mini is a good example of how far compact models have come. A model doesn't need to contain every possible capability to be useful for summarising notes, drafting simple text or answering straightforward questions. The trick is being realistic about its limitations, particularly when it comes to factual accuracy and complicated reasoning.

[ProperJobs50]

The gaming angle is interesting here too. Imagine having a small local model running alongside a game, keeping track of your notes, helping with builds or answering questions about quests without interrupting the experience. It doesn't need to be a genius at everything to be genuinely useful. Even a modest model could make a nice companion tool.

[Scarlett89]

One thing worth remembering is that token generation speed isn't the whole story. Prompt processing, context length and the way the model is quantised can all affect the experience on a Pi 5. A short conversation might feel perfectly manageable, while feeding it a huge document can make the same setup seem painfully slow. Matching the workload to the hardware matters.

[Megan81]

I like the privacy benefits, but let's not confuse local execution with complete security. A model running offline reduces the risk of your prompts being sent to a third party, which is a real advantage. You still need to protect the device, keep software updated and consider what other services or logs might expose your information. It's a useful layer of protection, not magic.

[SpinorWave]

There's also a learning benefit that gets overlooked. Setting up a model on a Pi forces you to understand memory limits, inference, model formats and performance tuning instead of treating AI as a black box. Even if you eventually decide the speed isn't suitable for daily use, you'll have learned something useful along the way. That's a pretty good outcome for a weekend project.

[Niamh88]

There's a certain charm to accepting that a computer doesn't have to be the fastest possible machine to be worthwhile. My first gaming PC took ages to load anything, and I still had a brilliant time with it. These days, a little local AI project feels similar: half the enjoyment is learning how the thing works, tweaking settings and gradually improving it