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SparkLab running frontier models on NVIDIA DGX Spark

Built for NVIDIA DGX Spark (GB10)

SparkLab

Documentation Latest release Apache License 2.0

Oakmind AI on X Oakmind AI on Hugging Face

Run frontier open-weight models privately on NVIDIA DGX Spark.

SparkLab is developed by SixteenMiles Labs, a research lab under Oakmind AI.

SparkLab is a GB10-native inference product for local, single-system deployments. It combines immutable model recipes, unified-memory admission, resumable checkpoint acquisition, FTW preparation, NVMe-backed MoE execution, and OpenAI- and Anthropic-compatible APIs.

Supported production target

SparkLab deliberately supports one narrow hardware profile:

  • NVIDIA GB10 Grace Blackwell Superchip (SM121)
  • 128 GB coherent unified memory
  • ARM64 Linux or DGX OS
  • NVIDIA driver r580 or newer and CUDA 13 toolkit
  • Local NVMe storage for checkpoints, FTW artifacts, and disk-backed experts
  • One local DGX Spark; multi-node and high-concurrency serving are outside the Beta scope

Platform, memory, swap, dependency, and storage requirements fail closed before a recipe launch. Unsupported hardware may still work through native runtime fallbacks, but it is not a SparkLab support claim.

Why SparkLab

  • GB10 admission: sparklab doctor validates architecture, CUDA, unified memory, swap, dependencies, NVMe backing, and free capacity with human and JSON output.
  • Immutable recipes: acquisition pins model revisions, records manifests, validates prepared artifacts, and rejects mismatched provenance.
  • Unified-memory planning: runtime admission budgets weights, expert cache, KV and recurrent state, workspaces, and an operating-system reserve without counting swap as capacity.
  • Frontier models beyond memory: FTW expert banks and bounded NVMe-backed caching let selected MoE checkpoints exceed physical memory.
  • Stable local APIs: OpenAI Chat Completions, Responses, and Anthropic Messages share one loopback endpoint with streaming, reasoning, and tool-call support.
  • Evidence-bound claims: model status and performance point to versioned, complete-checkpoint GB10 records rather than inferred capability.

Model portfolio

Model Parameters Quantization Status tok/s TTFT(s) Run
Fast — routine chat, editing, and short agent loops
Qwen3.6-35B-A3B 35B total / 3B active NVFP4 · FTW Experimental 67.46 0.320 Instructions
Frontier — quality-first coding, reasoning, and long agent work
Qwen3.8-Flash-Next 125B LM + 55B auxiliary / 6B active NVFP4 · FTW Experimental 12.58 0.786 Instructions
DeepSeek V4 Flash 284B total / 13B active DS-FP4 Preview 10.28 0.604 Instructions
GLM-5.3 Flash 320B total / 18B active NVFP4 + KDA FP8 · FTW Experimental 4.98 5.379 Instructions
Research — bounded execution outside the interactive envelope
GLM-5.2 753B total / 40B active NVFP4 Experimental 0.80 2.570 Instructions
Kimi K3 2.8T total / 16 of 896 experts ModelOpt NVFP4/FP8 · FTW Experimental 0.16 395.405 Instructions

Status meanings:

  • Experimental: implementation or measured evidence exists, but required gates remain incomplete or failed.
  • Preview: a bounded supported path exists, but the full release promise is incomplete.
  • Certified: the exact recipe, revision, artifact, and release environment passed all required correctness, parser, agent, context, latency, memory, NVMe, and endurance gates.

Run sparklab models --json for exact recipe versions, checkpoint revisions, artifact fingerprints, implementation state, evidence IDs, and known constraints.

Documentation

Contribution

SparkLab is stewarded in public by SixteenMiles Labs. Oakmind AI provides organizational backing, legal stewardship, and commercial support.

Credits and citation

SparkLab incorporates source and research contributions from FreeToken and builds on open inference projects including mini-sglang, SGLang, vLLM, FlashInfer, flash-linear-attention, LightLLM, and llama.cpp.

If you use SparkLab, cite the software:

@software{sixteenmileslabs2026sparklab,
  title={SparkLab: Frontier Open-Weight Model Inference on NVIDIA DGX Spark},
  author={{SixteenMiles Labs}},
  year={2026},
  url={https://github.com/sixteen-miles-labs/sparklab},
  license={Apache-2.0}
}

For work that builds on SparkLab's FreeToken-derived execution techniques, also cite the FreeToken paper:

@article{yang2026freetoken,
  title={FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution},
  author={Yang, Shuo and Fan, Xiaoze and Pan, Melissa and Xi, Haocheng and Wang, Zhe and Sun, Shanlin and Keutzer, Kurt and Han, Song and Zaharia, Matei and Xu, Chenfeng and Stoica, Ion},
  journal={arXiv preprint arXiv:2608.16157},
  year={2026}
}

Project links

Data verified by PyPI on Aug 29, 2026
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  • Released:
Newer release available (0.1.2)

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Avatar for lidaiqing from gravatar.com lidaiqing

Credits

Author/Maintainer: SixteenMiles Labs

GitHub Statistics

Data verified by PyPI on Aug 29, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev fi sgl accel

Tags

ai dgx-spark gb10 inference moe sparklab

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