I Ran a Local LLM on 12-Year-Old Raspberry Pi (It Actually Worked!)

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00:00:00This is the first-generation Raspberry Pi, which came out back in 2014.
00:00:05It has a 700 MHz single-core processor and 512 MB of RAM.
00:00:12By modern standards, this is basically a calculator.
00:00:16But today, we're going to see if we can push this 12-year-old hardware
00:00:21to its absolute limit by running a large language model on it locally.
00:00:26In this video, I'll show you which is the tiny model that you can run on a Raspberry Pi,
00:00:30and we'll see how it performs, and I'll show you how to install all the necessary dependencies
00:00:35so you can try it out for yourself.
00:00:37It's going to be a lot of fun, so let's dive into it.
00:00:40Honestly, I didn't think it was possible to find a model
00:00:47lean enough for this architecture.
00:00:49But after some digging, I actually found a candidate.
00:00:52Meet the Falcon H1 Tiny.
00:00:54It's an incredibly compact model with just 90 million parameters.
00:00:59It was developed by the Technology Innovation Institute in Abu Dhabi
00:01:03specifically to explore the extreme lower bounds of language modeling.
00:01:08But how could they make a model that small?
00:01:10Is there some kind of technical magic sauce behind it?
00:01:13Well, not really.
00:01:14They're basically using the same hybrid transformer plus Mamba architecture
00:01:19that companies like IBM used for their tiny Granite 4 models.
00:01:24Which I also did a video on if you want to check it out.
00:01:27But here's the thing.
00:01:28To successfully squeeze this model into memory, we have to talk about quantization.
00:01:33Now, the Falcon models are available in 2-bit, 4-bit, and 8-bit versions.
00:01:38Now, you might be tempted to try the ultra-lean IQ or importance quantization.
00:01:43But here's the catch.
00:01:45Those newer methods rely on a complex bit manipulation
00:01:49that requires modern CPU instructions to be efficient.
00:01:52On our vintage ARMv6 chip in our Raspberry Pi, this won't cut it.
00:01:57So instead, we have to go with the old-school Q4 models
00:02:01which is the gold standard for our case.
00:02:04It uses a medium-sized legacy quantization method
00:02:07that the Pi's processor can actually handle without choking.
00:02:11It gives us the best intelligent per megabyte ratio while keeping the logic intact.
00:02:17But to get this model running on this first-gen Raspberry Pi is not a trivial task.
00:02:22Since the Pi uses the ARMv6 architecture,
00:02:26it lacks the modern neon instructions that almost all of the AI libraries depend on.
00:02:31But luckily, there's llama.cpp which we can use to run our inference.
00:02:36But in order to do that, we have to compile its binary specifically for our ARMv6.
00:02:42And if you would try to compile it directly on the Pi,
00:02:45it would probably take 18 hours for the compiler to finish.
00:02:49That is, if it doesn't crash from out-of-memory errors first.
00:02:53So to bypass that, we have to get a little bit creative.
00:02:56We need to cross-compile these binaries on our laptop beforehand by using doc_cross,
00:03:02specifically targeting the ARMv6 instruction set with the VFP math unit enabled
00:03:08and then copy them over via SSH so we can get straight into inference.
00:03:13So that's exactly what we're going to do now.
00:03:15So first, we need to flash the leanest OS possible onto our Pi using the Raspberry Pi imager.
00:03:22And for a board with only 512 megabytes of RAM, every megabyte counts.
00:03:28So I'm going to go with Raspberry Pi OS Lite, the 32-bit version,
00:03:32since it doesn't have a desktop interface and it idles at a tiny fraction of the memory
00:03:38that the standard OS uses, leaving almost all of our RAM available for the model to run.
00:03:44And here's another important note, make sure to use the advanced settings
00:03:47to pre-configure your Wi-Fi and enable SSH.
00:03:51Because on these older boards, it's much easier to manage everything remotely,
00:03:55so you don't have to fight with that sluggish local terminal.
00:03:58Now, once the Pi is booted and we've SSH'd into it, we need to address the ARMv6 problem.
00:04:05So if we tried to compile llama CPP right here,
00:04:08the Pi would literally spend the next day and a half just chugging through headers.
00:04:13So instead, we're going to do it on a regular laptop to speed up compute and save time.
00:04:18So let's clone the source code of llama CPP and create a dedicated build
00:04:23directory where we will store our build that we'll be using on our Raspberry Pi.
00:04:28Now, here's another problem.
00:04:29My Mac is using ARMv8, which is the 64-bit version, not the 32-bit ARMv6.
00:04:37And they have different instruction sets.
00:04:40So to compile binary specifically for the Pi, we need to use doccross,
00:04:45which is a cross compiler toolchain that runs on my Mac,
00:04:48but generates binary specifically for the Pi's legacy architecture.
00:04:53Next, we need to configure the build.
00:04:55And this is where we need to be extremely precise.
00:04:58So we need to pass some very specific flags.
00:05:00First of all, we need to turn off shared libs to create a single portable binary.
00:05:05And then we have to turn off neon because our Pi lacks those modern math instructions.
00:05:10And we need to disable OpenMP to keep our memory footprint as lean as possible.
00:05:15We're essentially stripping out every modern luxury
00:05:18to ensure the binary is compatible with our old school Pi board.
00:05:22And now if we hit build in about two minutes, we should have a fully
00:05:26compiled optimized llama completion binary ready to be copied onto our Pi board.
00:05:31And now I'll use SSH to connect directly to my Pi via the network
00:05:35and create a fresh directory on the Pi and then SCP to copy our custom build binary onto it.
00:05:42And one last thing we need to do here.
00:05:44Let's go ahead and download the 2-bit, 4-bit and 8-bit legacy quantized models of the Falcon,
00:05:50because we'll be testing all of them sequentially.
00:05:53And then copy them to our Pi one by one over the network in the models folder.
00:05:58Now here comes the fun part.
00:05:59Let's move over to our Pi and execute our first inference test.
00:06:03We'll start with the most aggressive compression, the 2-bit quantized model.
00:06:07And here we need to run this long command.
00:06:10And basically what I'm doing here is prompting it with a simple
00:06:13"Hello, how are you?" and capping the output at 32 tokens.
00:06:18And we're specifying exactly one thread because, well, that's all we have.
00:06:22And we're also keeping the context size tiny at 128 tokens to save every possible byte of RAM.
00:06:29But the most important flag here is no M map.
00:06:32Typically, llama CPP uses memory mapping to load models, which is great for high-end GPUs,
00:06:38but it's a nightmare for our Pi board.
00:06:41On a 32-bit system with only 512 megabytes of RAM,
00:06:45M map can fail if it can't find a contiguous block of address space.
00:06:50So by disabling it, we force the model to load directly into the heap,
00:06:55giving us much more stable control over our limited memory.
00:06:58And with that said, let's run the command.
00:07:00And there it is, our first tokens.
00:07:03As we can see here, the 2-bit version is struggling a lot.
00:07:08First of all, we can see that it's processing a single token in like every three seconds,
00:07:14which is to be expected on an old Raspberry Pi board.
00:07:18But more importantly, the answer is just complete nonsense.
00:07:21On a 90 million parameter model, the weights are so compressed
00:07:25that the linguistic logic has basically collapsed.
00:07:28It's barely coherent, but technically it is working.
00:07:32So now let's see what happens if we swap it for the 4-bit model.
00:07:35And look at that, now we get a coherent greeting back.
00:07:40So that is a success.
00:07:42We now have an actual AI model running locally on the Pi
00:07:47and responding logically to our prompts.
00:07:49So yay!
00:07:50Now let's push it even further.
00:07:53Let's see if the Pi can handle an 8-bit model.
00:07:56And this time I'm going to ask it something more intelligent,
00:07:59like what is the capital of Albania?
00:08:02And well, that is just wrong because the capital of Albania is Tirana
00:08:08and that is clearly not factually correct.
00:08:10But if I ask what is the capital of Belgium, it responds correctly.
00:08:15So this is showing us something very interesting.
00:08:17It seems that the 90 million parameter crunch comes with its own cost.
00:08:22It might have accurate knowledge about larger, more popular countries,
00:08:26but lacks knowledge about lesser known countries and probably lesser known topics.
00:08:31And that is just the nature of knowledge.
00:08:33There is a finite amount of knowledge you can fit within those 90 million parameters.
00:08:38But nonetheless, the result is super cool.
00:08:41And this is a confirmation that yes, there are indeed AI models small enough
00:08:46and lean enough to run on a 12-year-old Raspberry Pi.
00:08:50Is it fast?
00:08:51Hell no.
00:08:52Is it precise?
00:08:53It might not be.
00:08:54Should you use it in production?
00:08:55Probably not.
00:08:57Unless you want to build a very, very, very, very slow robot.
00:09:02But most importantly, now we know that it is theoretically possible.
00:09:06So basically, that's all I wanted to prove in this video.
00:09:09And to be honest, this experiment was a lot of fun.
00:09:13So there you have it, folks.
00:09:14Those are the Falcon H1 Tiny models.
00:09:17Probably the smallest AI models currently out there.
00:09:20And now we know that they are in fact small enough to run on a first-gen Raspberry Pi,
00:09:25which is super cool.
00:09:27I can't stop celebrating how cool this fact is.
00:09:30Although the practical implementation of it is useless, it's still cool.
00:09:35So let me know, folks, if you have any funny thoughts,
00:09:37comments or remarks about what you just witnessed.
00:09:40Post them in the comment section down below.
00:09:42And folks, if you like these types of technical breakdowns,
00:09:45please let me know by smashing that like button underneath the video.
00:09:49And also don't forget to subscribe to our channel.
00:09:51This has been Andris from Better Stack, and I will see you in the next videos.

Key Takeaway

Running a local large language model on a 12-year-old first-generation Raspberry Pi requires stripping out modern CPU optimizations like neon instructions and memory mapping to fit the 90 million parameter Falcon H1 Tiny model into 512 MB of RAM.

Highlights

  • The 2014 first-generation Raspberry Pi operates on a 700 MHz single-core ARMv6 processor with 512 MB of RAM.

  • The Falcon H1 Tiny model features 90 million parameters and uses a hybrid transformer plus Mamba architecture.

  • Modern importance quantization methods fail on the vintage ARMv6 chip, making legacy Q4 quantization the optimal choice for compatibility.

  • Cross-compiling the llama.cpp binary on a laptop via doc_cross bypasses an estimated 18-hour compilation time on the Raspberry Pi hardware.

  • Disabling memory mapping via the no M map flag forces the language model directly into the heap to prevent allocation failures on 32-bit systems with limited RAM.

  • The 2-bit quantized Falcon H1 Tiny model processes one token every three seconds on the hardware but generates incoherent text.

  • The 4-bit and 8-bit quantized versions produce coherent logic, though the 90 million parameter size limits accurate geographic knowledge to major nations.

Timeline

Hardware Limitations and Model Selection

  • The first-generation Raspberry Pi from 2014 contains a 700 MHz single-core processor and 512 MB of RAM.
  • The Falcon H1 Tiny language model functions within extreme hardware limits due to a footprint of 90 million parameters.
  • The Technology Innovation Institute built this compact model using a hybrid transformer plus Mamba architecture.

Legacy hardware constraints demand highly specific software selections for local artificial intelligence execution. The 12-year-old computer possesses processing capabilities comparable to a modern calculator, which normally prevents language model deployment. Squeezing a neural network into this architecture requires a specialized model like the Falcon H1 Tiny. This specific 90 million parameter architecture matches the design choices used by corporate entities for compact model exploration.

Quantization Strategies for Legacy ARMv6 Chips

  • Modern importance quantization methods require contemporary CPU instruction sets to function efficiently.
  • Legacy Q4 quantization provides the optimal balance of intelligence and memory usage for vintage hardware.
  • The ARMv6 architecture cannot process the complex bit manipulation found in newer 2-bit, 4-bit, and 8-bit models.

Quantization choice determines whether vintage processing units can execute local inference without stalling. While advanced execution techniques minimize memory footprints, they rely on calculation methods that older hardware does not support. The standard Q4 approach bypasses these requirements by using a medium-sized legacy method that the chip can compute. This restriction ensures the underlying logic of the language model remains functional during processing.

Cross-Compilation and Operating System Optimization

  • Compiling the llama.cpp inference engine directly on the Raspberry Pi introduces a high risk of out-of-memory crashes and takes roughly 18 hours.
  • Using doc_cross on a secondary computer generates compatible binaries for the old ARMv6 instruction set with the VFP math unit enabled.
  • Raspberry Pi OS Lite saves critical system memory by eliminating the graphical desktop interface entirely.

Software preparation for legacy deployment requires offloading the compilation workload to modern machines. An ARMv8 laptop running a cross-compiler toolchain can output a single portable binary in approximately two minutes instead of forcing the Pi to process header files for days. The target operating system must also undergo minimization to maximize available memory space. Choosing the 32-bit Lite version leaves almost the entire 512 MB allocation open for model tasks by idling at a fraction of the standard memory load.

Inference Configuration and Memory Management

  • Compiling llama.cpp for this setup requires explicitly disabling shared libraries, neon instructions, and OpenMP features.
  • Disabling the standard memory mapping feature forces data allocation directly into the system heap.
  • Inference commands restrict processing to one thread and limit context size to 128 tokens to preserve memory.

Standard configuration settings for modern execution tools cause system failure on resource-constrained platforms. Disabling shared libraries and OpenMP features strips away modern multi-threading luxuries to protect the minimal memory pool. Standard memory mapping routinely fails on 32-bit systems when contiguous blocks of memory address space are unavailable. Forcing the model data into the heap stabilizes the execution parameters and allows the file transfer process to finish over the network.

Performance Evaluation and Knowledge Constraints

  • The 2-bit quantized model generates unintelligible text output at a speed of one token every three seconds.
  • The 4-bit quantized version successfully returns logical text responses to basic greetings.
  • Extreme parameter compression restricts the model factual accuracy to highly popular global topics.

Actual hardware execution reveals a clear trade-off between compression levels and output quality. High levels of compression cause the linguistic framework of a 90 million parameter model to break down into nonsense. Increasing the fidelity to a 4-bit or 8-bit structure resolves the structural errors but highlights severe limits in factual memory capacity. The final tests show that while the system can correctly identify the capital of Belgium, it fails on lesser-known countries like Albania due to the physical limits of the parameter size.

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