Run TinyLlama on a $10 Board with PicoLM – A Complete Tutorial

Embedded AI is no longer a luxury reserved for GPUs or cloud servers. PicoLM, a lightweight C‑only inference engine, lets you run an entire 1‑billion‑parameter model on a 256 MB device like the Raspberry Pi Zero 2W or the LicheeRV Nano. This tutorial walks through every step:

1. Why PicoLM? | TinyLlama 1.1B in GGUF format, zero dependencies, <80 KB binary, ~45 MB runtime RAM.
2. Hardware prerequisites – Raspberry Pi 5 (4‑core), Pi 4, Pi 3 B+ or Pi Zero 2W; or an RISC‑V board such as LicheeRV.
3. Build‑and‑install – one‑liner for Pi/Linux, full‑source build for Windows.
4. Download the model – a single 638 MB file; memory‑map the weights, no RAM copy.
5. Run a quick test – see the prompt‑to‑response pipeline.
6. Performance – token‑per‑second charts, RAM usage, and how to tweak threads.
7. Integrate with PicoClaw – the ultra‑light Go assistant that pipes prompts and reads JSON output.
8. Advanced options – JSON grammar constraints, KV cache persistence, mixed‑precision, GPU‑free offline use.
9. FAQ & troubleshooting – common pitfalls and tips for debugging.
10. Next steps – extending to Llama‑2, other LLaMA models, adding custom tools.

1. Why PicoLM?

Result – TinyLlama 1.1B runs on a ~$10 board with 256 MB RAM and 45 MB runtime memory.

2. Hardware prerequisites

You'll need a SD card with at least 1 GB storage for the model and runtime.

3. Build & install

One‑liner installer (Pi/Linux)

curl -sSL https://raw.githubusercontent.com/RightNow-AI/picolm/main/install.sh | bash

This script: 1. Detects your architecture (ARM64, ARMv7, x86‑64). 2. Installs gcc, make, and curl if missing. 3. Builds PicoLM with the optimal SIMD flags. 4. Downloads the TinyLlama 1.1B Q4_K_M model. 5. Adds picolm to your $PATH.

Full source build (cross‑platform)

git clone https://github.com/rightnow-ai/picolm.git
cd picolm/picolm
# Auto‑detect CPU for local build
make native
# Or cross‑compile for Raspberry Pi from an x86 host
make cross-pi
# Build for RISC‑V
make riscv

Windows (MSVC)

cd picolm
build.bat
picolm.exe model.gguf -p "Hello" -n 20

4. Download the model

The default make model target fetches TinyLlama 1.1B Chat (Q4_K_M) – 638 MB.

cd /opt/picolm
make model

If you prefer a different GGUF, place it under ~/.picolm/models/ and update the config later.

5. Quick test

./picolm /opt/picolm/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf -p "Explain gravity in one sentence" -n 30

You should see a short, coherent explanation. The binary size is about 80 KB and the program consumes <45 MB RAM.

6. Performance benchmarks

Tip: Use the -j flag to increase thread count (max 8 on Pi 5). On RISC‑V you’re limited to single‑threaded performance.

7. Integrate with PicoClaw

PicoClaw is a lightweight Go assistant that spawns PicoLM as a subprocess. Configure it to use the built binary:

{
  "agents": {
    "defaults": {"provider": "picolm", "model": "picolm-local"}
  },
  "providers": {
    "picolm": {
      "binary": "~/.picolm/bin/picolm",
      "model": "~/.picolm/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf",
      "max_tokens": 256,
      "threads": 4,
      "template": "chatml"
    }
  }
}

Run:

picoclaw agent -m "What is photosynthesis?"

PicoClaw handles the protocol, so your device remains offline, and the response appears instantly.

8. Advanced options

Example JSON test

./picolm /opt/picolm/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --json -p "Return JSON with name and age" -n 40 -t 0.3

You’ll receive something like {"name":"Alice","age":30}.

9. FAQ & troubleshooting

Q: My Pi stalls after a few seconds. A: The SD card might be slow; try a high‑speed UHS‑I card and enable write‑cache (ddrescue).

Q: I get an “Out‑of‑memory” error. A: Reduce -j or context length. For Pi Zero, keep context at 512.

Q: How much storage does the model take? A: 638 MB for TinyLlama 1.1B Q4_K_M. Other models vary (Llama‑2 7B = 4.1 GB).

Q: Can this run Llama‑2 7B? A: Yes, if your device has ~1.4 GB of runtime RAM for KV cache. Pi 4 with 4 GB will work, just slower.

10. Next steps

• Add custom tools: PicoClaw can call external binaries (e.g., web requesters) and embed responses in JSON.
• Support more models: Any GGUF LLaMA‑architecture works; download from Hugging Face and update the config.
• Improve speed: AVX2 or AVX‑512 kernels for desktop CPUs, speculative decoding, or larger SIMD kernels.
• Edge AI packaging: Bundle PicoLM into an app image for Raspberry Pi OS or Alpine Linux.

Bottom line

PicoLM demonstrates that a 1‑billion‑parameter LLM can run on a $10, 256 MB board without a GPU, cloud, or internet access. With a 2‑minute install, an 80 KB binary, and a 45 MB RAM footprint, it’s the cheapest local AI you’ll find. Whether you’re an IoT hobbyist, a privacy advocate, or a developer pushing the limits of edge inference, PicoLM gives you a fully offline, open‑source, and highly optimized LLM solution.