Djangodevreng/dgx-spark-benchmarks

DGX Spark LLM Arena benchmarks

Reproducible LLM inference benchmarks on an NVIDIA DGX Spark (GB10, 128 GB unified memory). Nine tests per model: five closed-loop (llama-benchy) and four open-loop (vllm bench serve). Quality scores come from vendor model cards.

• Hardware: NVIDIA DGX Spark (GB10, 128 GB unified memory)
• Creator: Django de Vreng (https://djangodevreng.nl)
• Visualised: https://djangodevreng.nl/arena/
• Raw runs: https://github.com/djangodevreng/dgx-spark-benchmarks
• Last updated: 2026-06-23

Configs

• models: one row per model (17 rows). Metadata, quality scores (MMLU-Pro, GPQA-Diamond, HumanEval/LiveCodeBench from vendor model cards) and the leaderboard composite scores per use-case preset.
• results: long format, one row per measured (model, benchmark) pair. 9 benchmarks: five closed-loop (llama-benchy) and four open-loop (vllm bench serve).

from datasets import load_dataset

models = load_dataset("Djangodevreng/dgx-spark-benchmarks", "models")
results = load_dataset("Djangodevreng/dgx-spark-benchmarks", "results")

Method

Three runs per benchmark, fixed seed (42), reported as the mean. Run-to-run variance stays within about 2%. No latency gate: slow models stay visible. Full methodology and the raw stdout per run live in this repo.

License

CC-BY-4.0. Free to use, including commercially, with attribution: Django de Vreng, https://djangodevreng.nl.

Citation

@misc{devreng-dgx-spark-benchmarks-2026,
  author = {Django de Vreng},
  title  = {DGX Spark LLM Arena benchmarks},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Djangodevreng/dgx-spark-benchmarks}
}