---
name: freetoken-build-dankclaw
description: "Build FreeToken on Dankclaw (Ubuntu 24.04) with CUDA — installs nvidia-cuda-toolkit, sets CUDA_HOME=/usr, overrides nvcc/CUDA version mismatch"
---

# FreeToken Build on Dankclaw

Dankclaw is Ubuntu 24.04 with ROCm already installed. FreeToken builds for **CUDA** (not ROCm) because the kernel package (`_pinned_tensor`) links against `cudart`.

## Prerequisites (install if missing)

```bash
export PATH="/home/dank/.local/bin:$PATH"
export CUDA_HOME=/usr
export FREETOKEN_ALLOW_CUDA_MISMATCH=1

# CUDA toolkit (if not present):
sudo apt-get install -y nvidia-cuda-toolkit
# UV (if not present):
curl -LsSf https://astral.sh/uv/install.sh | sh
```

## Key environment variables

|Variable|Value|Why|
|---|---|---|
|`CUDA_HOME`|`/usr`|Ubuntu CUDA toolkit package places nvcc at `/usr/bin/nvcc` and libcudart at `/usr/lib/x86_64-linux-gnu/`|
|`FREETOKEN_ALLOW_CUDA_MISMATCH=1`|Set|nvcc is 12.4 but torch 2.11.0+cu130 ships CUDA 13.0 — safe to override|
|`PATH`|`/home/dank/.local/bin:$PATH`|uv installs here|

## Build procedure

```bash
cd /home/dank
git clone https://github.com/FlashML-org/FreeToken.git
cd FreeToken
uv venv && source .venv/bin/activate
uv pip install -e ".[accel]"
```

## Verification

```bash
ft --version                    # → freetoken version 0.1.2
python3 -c "import torch; print(torch.__version__)"  # → 2.11.0+cu130
python3 -c "from freetoken.kernel import _pinned_tensor; print(_pinned_tensor.__file__)"
python3 -c "import flashinfer; print(flashinfer.__version__)"  # → 0.6.18
python3 -c "import sgl_kernel; print(sgl_kernel.__version__)"  # → 0.4.5
```

## Notes

- `torch.cuda.is_available()` may return `False` with "Can't initialize NVML" warning — this is the management library, not CUDA runtime. The kernels are built and linked correctly.
- The NVIDIA driver modules may or may not be loaded; they don't affect the build.
- ROCm is present on Dankclaw but FreeToken's kernel build specifically requires CUDA (links `cudart`).