---
title: "Run models with llama.cpp on DGX Spark — Troubleshooting"
canonical: "https://build.nvidia.com/spark/llama-cpp/troubleshooting.md"
---

| Symptom | Cause | Fix |
|---------|-------|-----|
| `cmake` fails with "CUDA not found" | CUDA toolkit not in PATH | Run `export PATH=/usr/local/cuda/bin:$PATH` and re-run CMake from a clean build directory |
| Build errors mentioning wrong GPU arch | CMake `CMAKE_CUDA_ARCHITECTURES` does not match GB10 | Use `-DCMAKE_CUDA_ARCHITECTURES="121"` for DGX Spark GB10 as in the instructions |
| GGUF download fails or stalls | Network or Hugging Face availability | Re-run `hf download`; it resumes partial files |
| "CUDA out of memory" when starting `llama-server` | Model too large for current context or VRAM | Lower `--ctx-size` (e.g. 4096) or use a smaller quantization from the same repo |
| Server runs but latency is high | Layers not on GPU | Confirm `--n-gpu-layers` is high enough for your model; check `nvidia-smi` during a request |
| `curl: (7) Failed to connect` on port 30000 | No listener yet, wrong host, or crash | Wait for `server is listening`; run `curl` on the same host as `llama-server` (or Spark’s IP); run `ss -tln` and confirm `:30000`; read server stderr for OOM or bad `--model` path |
| Chat API errors or empty replies | Wrong `--model` path or incompatible GGUF | Verify the path to the `.gguf` file; update llama.cpp if the GGUF requires a newer format |

> [!NOTE]
> DGX Spark uses Unified Memory Architecture (UMA), which allows flexible sharing between GPU and CPU memory. Some software is still catching up to UMA behavior. If you hit memory pressure unexpectedly, you can try flushing the page cache (use with care on shared systems):
```bash
sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
```

For the latest platform issues, see the [DGX Spark known issues](https://docs.nvidia.com/dgx/dgx-spark/known-issues.html) documentation.