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Dria Node v2 — Tester Guide

Thanks for testing! This guide walks you through building and running a Dria compute node from source.

1. Install Prerequisites

You need Rust and cmake. Pick your OS:

macOS

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source ~/.cargo/env

# Install cmake
brew install cmake

Linux (Ubuntu/Debian)

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source ~/.cargo/env

# Install build tools
sudo apt-get update && sudo apt-get install -y cmake build-essential

Windows

Open PowerShell as Administrator (right-click Start → "Terminal (Admin)") and run these commands one by one:

# Install Rust
winget install Rustlang.Rustup

# Install C++ build tools (needed to compile the inference engine)
winget install Microsoft.VisualStudio.2022.BuildTools --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;includeRecommended"

# Install CMake
winget install -e --id Kitware.CMake

# Install LLVM/Clang (needed by bindgen for llama.cpp bindings)
winget install -e --id LLVM.LLVM

Important: After all three finish, close PowerShell and open a new one so the tools are available. To verify everything installed correctly:

rustc --version
cmake --version

Both should print a version number. If either says "not recognized", restart your PC and try again.

2. Build

git clone https://github.com/firstbatchxyz/dkn-compute-node.git
cd dkn-compute-node
git checkout v2
cargo build --release

This takes a few minutes (it compiles the inference engine from source).

Apple Silicon (M1/M2/M3/M4)? Build with Metal GPU support instead:

cargo build --release --features metal

NVIDIA GPU? Install the CUDA Toolkit first, then:

cargo build --release --features cuda

3. Run Setup

The setup wizard helps you pick and download a model:

./target/release/dria-node setup

Windows (PowerShell): Use backslashes and .exe:

.\target\release\dria-node.exe setup

It will:

  • Detect your RAM and show models that fit
  • Let you pick a model and quantization
  • Download it (once, cached for future runs)
  • Run a test inference to confirm everything works

If you're unsure which model to pick, start with lfm2.5:1.2b — it's the smallest (~0.8 GB) and works on any machine.

4. Your Wallet Key

You'll need your Ethereum wallet private key. The node uses it to sign messages and prove identity on the network.

This is the 64-character hex string (with or without 0x prefix). You can export it from MetaMask: Account Details → Show Private Key.

5. Start the Node

./target/release/dria-node start \
  --wallet YOUR_KEY_HERE \
  --model lfm2.5:1.2b

Windows (PowerShell):

.\target\release\dria-node.exe start --wallet YOUR_KEY_HERE --model lfm2.5:1.2b

Replace YOUR_KEY_HERE with the key from step 4, and lfm2.5:1.2b with whatever model you chose in setup.

If you have a GPU and built with --features metal or --features cuda:

./target/release/dria-node start \
  --wallet YOUR_KEY_HERE \
  --model lfm2.5:1.2b \
  --gpu-layers -1

What to expect

INFO node identity                         address=0x...
INFO benchmark complete                    tps=25.3 model=lfm2.5:1.2b
INFO connected to router                   node_id=... router=quic.dria.co:4001
INFO node ready                            models=["lfm2.5:1.2b"] online=true

That's it — the node is running and accepting tasks. Leave it open. Press Ctrl+C to stop.

6. Skip the Flags Next Time

Instead of typing flags every time, set environment variables:

# Add these to your shell profile (~/.bashrc, ~/.zshrc, etc.)
export DRIA_WALLET=your_key_here
export DRIA_MODELS=lfm2.5:1.2b
export DRIA_GPU_LAYERS=-1

Then just run:

./target/release/dria-node start

Models

Model Type Download Min RAM
qwen3.5:0.8b Text, Vision ~0.5 GB ~1 GB
lfm2.5:1.2b Text ~0.8 GB ~1 GB
lfm2.5-audio:1.5b Text, Audio ~1.0 GB ~1.5 GB
lfm2.5-vl:1.6b Text, Vision ~1.2 GB ~1.5 GB
qwen3.5:2b Text, Vision ~1.2 GB ~2 GB
nanbeige:3b Text ~2.0 GB ~2.5 GB
locooperator:4b Text ~2.5 GB ~3 GB
qwen3.5:9b Text, Vision ~6.0 GB ~7 GB
lfm2:24b-a2b Text ~14 GB ~16 GB
qwen3.5:27b Text, Vision ~16 GB ~18 GB
qwen3.5:35b-a3b Text, Vision ~20 GB ~22 GB
nemotron:30b-a3b Text ~24.5 GB ~27 GB

Pick one model that fits your RAM. Smaller models are faster to download and easier to test with.

All Options

Flag Env Var Default What it does
--wallet DRIA_WALLET (required) Your node identity key
--model DRIA_MODELS (required) Model(s) to serve
--router-url DRIA_ROUTER_URL quic.dria.co:4001 Router to connect to
--gpu-layers DRIA_GPU_LAYERS 0 (CPU) GPU layers (-1 = all)
--max-concurrent DRIA_MAX_CONCURRENT 1 Parallel inference tasks
--data-dir DRIA_DATA_DIR ~/.dria Where models are cached
--quant DRIA_QUANT Q4_K_M Override quantization
--insecure DRIA_INSECURE false Skip TLS verification
--skip-update DRIA_SKIP_UPDATE false Skip auto-update check

Troubleshooting

Windows: "dria-node is not recognized" On Windows you must use .\target\release\dria-node.exe (backslashes, .exe extension). PowerShell does not find executables without the .exe suffix.

"cmake not found" or build errors about C compiler Make sure cmake is installed (step 1). On macOS: brew install cmake. On Linux: sudo apt install cmake build-essential. On Windows: winget install -e --id Kitware.CMake then reopen PowerShell.

Windows: "dria-node.exe not found in target\release" The build probably failed. Scroll up in your terminal and look for red error messages. The most common cause is missing C++ build tools — run winget install Microsoft.VisualStudio.2022.BuildTools --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;includeRecommended", reopen PowerShell, and rebuild with cargo build --release.

Windows: "Unable to find libclang" or "couldn't find clang.dll" Install LLVM: winget install -e --id LLVM.LLVM, reopen PowerShell, and rebuild. If it still can't find it, set the path manually: $env:LIBCLANG_PATH = "C:\Program Files\LLVM\bin" then rebuild.

Build fails Try a clean build: cargo clean && cargo build --release. Make sure you're on the v2 branch: git checkout v2.

"unknown model" Model names are exact. Use the names from the table above (e.g. lfm2.5:1.2b, not lfm-2.5).

"all routers unavailable" or "offline mode" The node can't reach the router. Check your internet connection. If you're behind a strict firewall, UDP port 4001 outbound must be allowed.

Slow inference If you have a GPU, make sure you built with --features metal (Mac) or --features cuda (NVIDIA) and are passing --gpu-layers -1.

Model download stalls or fails Models come from HuggingFace. Try again — it might be a temporary network issue. You can also set HF_ENDPOINT if HuggingFace is blocked in your region.

Want more detail in the logs?

RUST_LOG=debug ./target/release/dria-node start ...

Reporting Issues

If something goes wrong, please share:

  1. Your OS and hardware (CPU, RAM, GPU)
  2. The command you ran
  3. The full error output