---
title: "cuTile Kernels — Troubleshooting"
canonical: "https://build.nvidia.com/spark/cutile-kernels/troubleshooting.md"
---

| Symptom | Cause | Fix |
|---------|-------|-----|
| `docker: permission denied` | User not in docker group | `sudo usermod -aG docker $USER && newgrp docker` |
| `401 Client Error: Unauthorized` | Missing HuggingFace token | `export HF_TOKEN=<your_token>` |
| `ModuleNotFoundError: tilegym` | TileGym not installed | `cd TileGym && pip install .` |
| `RuntimeError: CUDA out of memory` | Model too large | Reduce batch size or use smaller model |
| `Killed` during model load | Out of system memory | Clear cache: `sync; echo 3 > /proc/sys/vm/drop_caches` |
| Slow first run | JIT compilation | Normal - cuTile compiles kernels on first run |
| `FileNotFoundError: input_prompt_small.txt` | Missing input file | Run from `modeling/transformers` directory |
| `torch.cuda.OutOfMemoryError` | Insufficient GPU memory | Reduce `--batch_size` parameter |
| `ImportError: cuda.tile` | Missing Tile IR | Install: `apt-get install cuda-tile-ir-13-2` |
| Benchmark hangs | GPU busy or locked | Check `nvidia-smi` for other processes |

> [!NOTE] 
> DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. 
> With many applications still updating to take advantage of UMA, you may encounter memory issues even when within 
> the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:

```bash
sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
```

> [!TIP]
> First run of cuTile kernels includes JIT compilation overhead. Subsequent runs will be faster as compiled kernels are cached.

For the latest known issues, please review the [DGX Spark User Guide](https://docs.nvidia.com/dgx/dgx-spark/known-issues.html).