dgx-spark-playbooks/nvidia/llama-cpp

Run models with llama.cpp on DGX Spark

Build llama.cpp with CUDA and serve models via an OpenAI-compatible API (Qwen3.6 as example)

Table of Contents

• Overview
• Instructions
• Troubleshooting

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Overview

Basic idea

llama.cpp is a lightweight C/C++ inference stack for large language models. You build it with CUDA so tensor work runs on the DGX Spark GB10 GPU, then load GGUF weights and expose chat through llama-server’s OpenAI-compatible HTTP API.

This playbook walks through that stack end to end using Qwen3.6 as the hands-on example: a current-generation family that runs well from quantized GGUF on Spark. Checkpoint choices and paths for all supported models are summarized in the matrix below; commands are in the instructions.

What you'll accomplish

You will build llama.cpp with CUDA for GB10, download a Qwen3.6 example checkpoint, and run llama-server with GPU offload. You get:

• Local inference through llama.cpp (no separate Python inference framework required)
• An OpenAI-compatible /v1/chat/completions endpoint for tools and apps
• A concrete validation that the Qwen3.6 example runs on this stack on DGX Spark

What to know before starting

• Basic familiarity with Linux command line and terminal commands
• Understanding of git and building from source with CMake
• Basic knowledge of REST APIs and cURL for testing
• Familiarity with Hugging Face Hub for downloading GGUF files

Prerequisites

Hardware requirements

• NVIDIA DGX Spark with GB10 GPU
• Sufficient unified memory for the example UD-Q4_K_M MoE checkpoint (weights on the order of ~20GB, plus KV cache and runtime overhead—scale up if you pick a larger quant or longer context)
• At least ~30GB free disk for the example download plus build artifacts (more if you keep multiple GGUFs)

Software requirements

• NVIDIA DGX OS
• Git: git --version
• CMake (3.14+): cmake --version
• CUDA Toolkit: nvcc --version
• Network access to GitHub and Hugging Face

Model support matrix

The following models are supported with llama.cpp on Spark. The instructions use the Qwen3.6 example row by default.

Model	Support Status	HF Handle
Qwen3.6-35B-A3B (example walkthrough)	✅	unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf
Qwen3.6-27B	✅	unsloth/Qwen3.6-27B-GGUF/Qwen3.6-27B-Q4_K_M.gguf
Gemma 4 31B IT	✅	ggml-org/gemma-4-31B-it-GGUF
Gemma 4 26B A4B IT	✅	ggml-org/gemma-4-26B-A4B-it-GGUF
Gemma 4 E4B IT	✅	ggml-org/gemma-4-E4B-it-GGUF
Gemma 4 E2B IT	✅	ggml-org/gemma-4-E2B-it-GGUF
Nemotron-3-Nano	✅	unsloth/Nemotron-3-Nano-30B-A3B-GGUF

Time & risk

• Estimated time: About 30 minutes, plus downloading the example GGUF (~20GB order of magnitude for the default quant)
• Risk level: Low — build is local to your clone; no system-wide installs required for the steps below
• Rollback: Remove the llama.cpp clone and the model directory under ~/models/ to reclaim disk space
• Last updated: 04/27/2026
  • We now walk you through Qwen3.6 first; other models remain in the list

Instructions

Step 1. Verify prerequisites

The example checkpoint is Qwen3.6-35B-A3B-UD-Q4_K_M.gguf from Hugging Face repo unsloth/Qwen3.6-35B-A3B-GGUF (full handle: unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf). The other supported file is Qwen3.6-27B-Q4_K_M.gguf from unsloth/Qwen3.6-27B-GGUF—use the same build and server steps, changing hf download and --model paths (see the overview model matrix).

Ensure the required tools are installed:

git --version
cmake --version
nvcc --version

All commands should return version information. If any are missing, install them before continuing.

Install the Hugging Face CLI:

python3 -m venv llama-cpp-venv
source llama-cpp-venv/bin/activate
pip install -U "huggingface_hub[cli]"

Verify installation:

hf version

Step 2. Clone the llama.cpp repository

Clone upstream llama.cpp—the framework you are building:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp

Step 3. Build llama.cpp with CUDA

Configure CMake with CUDA and GB10’s sm_121 architecture so GGML’s CUDA backend matches your GPU:

mkdir build && cd build
cmake .. -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="121" -DLLAMA_CURL=OFF
make -j8

The build usually takes on the order of 5–10 minutes. When it finishes, binaries such as llama-server appear under build/bin/.

Step 4. Download example Qwen3.6-35B-A3B GGUF

llama.cpp loads models in GGUF format. This playbook uses the UD-Q4_K_M quantized MoE checkpoint from Unsloth, which fits comfortably on DGX Spark GB10 unified memory while keeping strong quality.

hf download unsloth/Qwen3.6-35B-A3B-GGUF \
  Qwen3.6-35B-A3B-UD-Q4_K_M.gguf \
  --local-dir ~/models/Qwen3.6-35B-A3B-GGUF

The file is on the order of ~20GB (exact size may vary). The download can be resumed if interrupted.

Step 5. Start llama-server with Qwen3.6-35B-A3B

From your llama.cpp/build directory, launch the OpenAI-compatible server with GPU offload:

./bin/llama-server \
  --model ~/models/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf \
  --host 0.0.0.0 \
  --port 30000 \
  --n-gpu-layers 99 \
  --ctx-size 8192 \
  --threads 8

Parameters (short):

• --host / --port: bind address and port for the HTTP API
• --n-gpu-layers 99: offload layers to the GPU (adjust if you use a different model)
• --ctx-size: context length (can be increased up to model/server limits; uses more memory)
• --threads: CPU threads for non-GPU work

You should see log lines similar to:

llama_new_context_with_model: n_ctx = 8192
...
main: server is listening on 0.0.0.0:30000

Keep this terminal open while testing. Large GGUFs can take a minute or more to load; until you see server is listening, nothing accepts connections on port 30000 (see Troubleshooting if curl reports connection refused).

Step 6. Test the API

Use a second terminal on the same machine that runs llama-server (for example another SSH session into DGX Spark). If you run curl on your laptop while the server runs only on Spark, use the Spark hostname or IP instead of localhost.

curl -X POST http://127.0.0.1:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma4",
    "messages": [{"role": "user", "content": "New York is a great city because..."}],
    "max_tokens": 100
  }'

If you see curl: (7) Failed to connect, the server is still loading, the process exited (check the server log for OOM or path errors), or you are not curling the host that runs llama-server.

Example shape of the response (fields vary by llama.cpp version; message may include extra keys):

{
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "New York is a great city because it's a living, breathing collage of cultures, ideas, and possibilities—all stacked into one vibrant, never‑sleeping metropolis. Here are just a few reasons that many people ("
      }
    }
  ],
  "created": 1765916539,
  "model": "Qwen3.6-35B-A3B-UD-Q4_K_M.gguf",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 100,
    "prompt_tokens": 25,
    "total_tokens": 125
  },
  "id": "chatcmpl-...",
  "timings": {
    ...
  }
}

Step 7. Longer completion (with Qwen3.6)

Try a slightly longer prompt to confirm stable generation with Qwen3.6-35B-A3B:

curl -X POST http://127.0.0.1:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "messages": [{"role": "user", "content": "Solve this step by step: If a train travels 120 miles in 2 hours, what is its average speed?"}],
    "max_tokens": 500
  }'

Step 8. Cleanup

Stop the server with Ctrl+C in the terminal where it is running.

To remove this tutorial’s artifacts:

rm -rf ~/llama.cpp
rm -rf ~/models/Qwen3.6-35B-A3B-GGUF

Deactivate the Python venv if you no longer need hf:

deactivate

Step 9. Next steps

1. Context length: Increase --ctx-size for longer chats (watch memory; 1M-token class contexts are possible only when the build, model, and hardware allow).
2. Other models: Point --model at any compatible GGUF; the llama.cpp server API stays the same.
3. Integrations: Point Open WebUI, Continue.dev, or custom clients at http://<spark-host>:30000/v1 using the OpenAI client pattern.

The server implements the usual OpenAI-style chat features your llama.cpp build enables (including streaming and tool-related flows where supported).

Troubleshooting

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):

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

For the latest platform issues, see the DGX Spark known issues documentation.