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FujitsuPolycom/sparkring

Low-latency collective transport and distributed LLM serving stack for switchless GB10 clusters. (简介来自仓库自身文档。)

数据截至 10月27日FujitsuPolycom

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项目状态许可证: Apache-2.0最近 push: 10月19日Open Issues(含 PR): 7

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项目文档

SparkRing

SparkRing's profiles run three models created by Local Inference Lab, Inc. and licensed under the Local Inference Lab License, Version 1.0: GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD (glm53-flash-tp2, glm53-flash-tp4, glm53-flash-tp2-dcp2), GLM-5.3-NVFP4-CSF (glm53-nvfp4-csf-tp6, glm53-nvfp4-csf-tp8) and Qwen3.8-Flash-Next-NVFP4-MXFP8-CSF-QAD (qwen38-flash-next-tp2). https://local-inference-lab.ai/

SparkRing serves large language models on two to eight NVIDIA DGX Sparks cabled directly over ConnectX-7, with no switch. One command installs a model on all of them, or several models on separate groups of them.

Quick start

  1. Cable two to eight Sparks directly: a pair from p0 to p0, or a line or ring from each Spark's p0 to the next Spark's p1 (requirements).
  2. Pick a --profile from the table below, or build the commands in the Install Builder.
  3. On the Spark connected to your network, run:
curl -fsSL https://fujitsupolycom.github.io/sparkring/install.sh | bash -s -- --profile PROFILE

This installs the release the Install Builder describes; the pinned install command installs main or another commit.

The installer asks before it changes anything: --plan previews and --yes approves, and running it again updates or switches models. --on 0-3 places a model on part of the Sparks. Node A restarts a model that stops serving; --no-auto-recover turns that off.

More: documentation · commands · status dashboard · Docker Compose files · pinned install command

SIRCL

SIRCL, the Switchless Inference RDMA Collective Layer, carries the model's collectives between Sparks, including Sparks reached through another Spark's ConnectX-7 relay (architecture). net-sircl brings it to any NCCL application as a network plugin (research-only).

Profiles

Model Checkpoint Sparks --profile value Context KV tokens Thinking
Qwen3.8-Flash-Next CSF (NVFP4/MXFP8), Local Inference Lab 2 qwen38-flash-next-tp2 262K 2.5M on · xhigh
Qwen3.8-Flash-Next NVFP4 QAD, Local Inference Lab 4 qwen38-flash-next-tp4 262K 2.8M on · xhigh
GLM-5.3-Flash CSF (NVFP4/MXFP8) 2 glm53-flash-tp2 1M 1.5M always · high
GLM-5.3-Flash CSF (NVFP4/MXFP8), DCP2 2 glm53-flash-tp2-dcp2 1M 4.1M always · high
GLM-5.3-Flash CSF (NVFP4/MXFP8) 4 glm53-flash-tp4 1M 5.7M always · high
GLM-5.3 NVFP4, Local Inference Lab 8 glm53-nvfp4-tp8 1M 3.4M always · high
MiMo-V2.6-Flash-MOPD Xiaomi MiMo 2 mimo-v26-flash-mopd-tp2 262K 656K on
MiMo-V2.6-Flash-MOPD Xiaomi MiMo 4 mimo-v26-flash-mopd-tp4 262K 2.2M on
DeepSeek-V4.1-Flash FP8/MXFP4, DeepSeek 4 deepseek-v41-flash-tp4 1M 12.8M on · high
Swift-1.5-Qwen3.8-Flash-Next NVFP4 experts/BF16, UkisAI 2 swift15-qwen38-flash-next-tp2 262K 2.9M on · xhigh
Swift-1.5-Qwen3.8-Flash-Next NVFP4 experts/BF16, UkisAI 4 swift15-qwen38-flash-next-tp4 262K 3.1M on · xhigh

glm53-flash-tp2-dcp2 splits the KV cache across the pair (decode-context parallelism 2). It, glm53-nvfp4-tp8 and swift15-qwen38-flash-next-tp4 are research-only; the other rows are implemented. The six-Spark GLM-5.3 profile glm53-nvfp4-csf-tp6 runs on positions 0-5 of an eight-Spark ring (research-only). More profiles and options: profile catalog.

Speed

Output tok/s, measured with llm-inference-bench 0.7.6 at temperature 1 on release 2026.10.2's image, one start per profile installed without research options; decode and prefill at 32K context. The default image, release 2026.10.5's, has no speed record of its own, nor has its parent, release 2026.10.4's. Records: every cell's raw source, rings of four, ring of eight, paths of four.

Model Checkpoint Sparks --profile Decode, 1 / 4 / 8 streams Prefill, 32K prompt Coding peak Layout
DeepSeek-V4.1-Flash FP8/MXFP4 8 deepseek-v41-flash-tp8 74 / 174 / 259 4,851 134 ring of eight
DeepSeek-V4.1-Flash FP8/MXFP4 4 deepseek-v41-flash-tp4 71 / 137 / 194 4,388 102 ring of four
DeepSeek-V4.1-Flash FP8/MXFP4 4 deepseek-v41-flash-tp4 70 / 139 / 198 4,426 102 path of four
GLM-5.3 NVFP4 8 glm53-nvfp4-tp8 46 / 94 / 129 1,387 51 ring of eight
GLM-5.3-Flash CSF 4 glm53-flash-tp4 68 / 135 / 204 3,165 80 path of four
GLM-5.3-Flash CSF 2 glm53-flash-tp2 38 / 83 / 113 2,420 49 pair
Qwen3.8-Flash-Next QAD step 5500 4 qwen38-flash-next-tp4 73 / 204 / 288 4,553 112 ring of four
Qwen3.8-Flash-Next QAD step 5500 4 qwen38-flash-next-tp4 78 / 210 / 274 4,377 111 path of four
Qwen3.8-Flash-Next CSF 2 qwen38-flash-next-tp2 68 / 150 / 219 3,716 91 pair

Documentation

Acknowledgements

SparkRing builds on Local Inference Lab (GitHub · Hugging Face · Discord): the B12X kernel library, the Qwen3.8-Flash-Next and GLM-5.3-Flash Spark NVFP4 checkpoints, llm-inference-bench and their vLLM fork.

SparkRing includes software derived from RoCEnante (b12x/comm/roce, Local Inference Lab), written by Jason (original-el8) in local-inference-lab/b12x PR 295, with dual-rail striping and graph-collective work by Luke Alonso (lukealonso), licensed under Apache-2.0; b12x now lives in FlashInfer at flashinfer/experimental/b12x (provenance).

SparkRing's NCCL patches follow Joseph Rose's nccl-spark-switchless. Thanks to Eugene Rakhmatulin for the eugr/spark-vllm-b12x base image, and to the vLLM, NVIDIA NCCL and ExLlamaV3 contributors. The third-party notices give origins and licenses.

License

SparkRing code is Apache-2.0. Model weights and bundled components keep their own terms; check the model cards and third-party notices.

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