JSpark3 v1

Three DGX Sparks, one OpenAI-compatible GLM-5.3 Flash endpoint, with the recipe and evidence to rebuild it.

JSpark3 is the measured three-Spark path for the EXL3/TR3 checkpoint in this repository. It combines the exact mirrored weights with a pinned serving stack and a fail-closed fleet controller.

Results

Measured result	JSpark3 v1
Single-stream code decode	1.49x faster, 66.3 vs 44.6 tok/s on the two-Spark recipe
sparkDash clamp-code time to first token	391 ms, down from 719 ms on two Sparks
Four-stream aggregate decode	251 tok/s, up from 146.5 tok/s on two Sparks
Same agent task and prompt	1.8x the throughput, 44.6 vs 24.7 tok/s on two Sparks
Configured context	1,000,000 tokens

The code-screen comparison and agent task were run on the JSpark3 fleet against the compatibility-adapted current Mia two-Spark recipe. sparkDash used the same pinned author protocol as Mia's published figures, on separate fleets and dates. See the machine-readable results and exact figures, receipts, and caveats.

What you get

• An exact, hash-verifiable mirror of Brandon M. Music's ShapleyMcg EXL3/TR3 quantization, as re-hosted by Mia-AiLab, with its provenance intact.
• A TP3 and EP3 serving recipe with DFlash2, FP8 KV cache, prefix caching, and a selective W8A16 trunk overlay.
• A fail-closed preflight, start, health, and verification path.
• Public measurements with the regressions and missed gates left in.

Install and run it with the GitHub recipe.

License and weight provenance: JSpark3's original recipe code and documentation are Apache-2.0. The weights here are Brandon M. Music's exact EXL3/TR3 quantization, re-hosted byte-for-byte by Mia-AiLab and mirrored by JSpark3 without changing a weight byte. They remain under the attribution-required, source-available ShapleyMcg License v1.0, including its named exclusion; Z.AI's base model remains MIT. DFlash2 is not mirrored and remains a separate CC BY-NC-ND 4.0 dependency.

JSpark3 turns three NVIDIA DGX Sparks into one fast GLM-5.3 Flash server, with a reproducible TP3 recipe and public benchmarks. The recipe uses tensor parallel 3, expert parallel 3, a two-leg RoCE-v2 triangle, EXL3/TR3 4-bpw target weights, a DFlash2 k=7 draft, FP8 KV cache, prefix caching, a 1,000,000-token configured context, and a selective INT8 (W8A16 Marlin) overlay for the model trunk.

This public repository is the release home for the card, results, license set, and attributed target-weight mirror. All 123 allowlisted Git LFS payloads were verified by size and SHA-256 against the pinned manifest, and jspark3/MIRROR-COMPLETION.json was verified byte for byte before merge into the public main revision. The recipe, documentation, and evidence are in the v1.0.0 GitHub release, released 2026-09-02.

Weights

These weights are not ours. This repository carries an exact, hash-verifiable mirror of Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw at revision 25a44fdbf16862a46b7cc9921142c6c81350af2f, which is itself a byte-identical re-host of brandonmusic/GLM-5.3-Flash-tr3-4bpw at revision 5ab363a8dcf6405955fd5f99671e01a1c9fb124b. Brandon M. Music is the quantization author; Z.AI created the base model. JSpark3 trained nothing, quantized nothing, and modified no weight byte.

The mirror is described file by file in jspark3/WEIGHTS-MANIFEST.json: every file at the pinned revision with its size, SHA-256, and how that hash was obtained. The chain, the verification method, and one recorded discrepancy in the upstream checksum file are in jspark3/PROVENANCE.md. The upstream card is preserved verbatim as UPSTREAM_MODEL_CARD.md, and every other upstream file keeps its exact upstream path so that a checkpoint contract validating a download from this mirror validates exactly as it does upstream.

The DFlash2 speculative draft is a separate checkpoint from Inco AI under CC BY-NC-ND 4.0. It is not mirrored here; operators fetch it from its own repository at its own pinned revision.

Hub status: all 123 allowlisted Git LFS payloads and jspark3/MIRROR-COMPLETION.json were remotely verified before merge into the public main revision. See jspark3/UPLOAD.md for the recorded procedure.

Pinned inputs

Input	Identity
Target checkpoint	Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw at 25a44fdbf16862a46b7cc9921142c6c81350af2f, declared byte-identical to brandonmusic/GLM-5.3-Flash-tr3-4bpw at 5ab363a8dcf6405955fd5f99671e01a1c9fb124b
Draft checkpoint	incoai/GLM-5.3-Flash-DFlash2 at dc77ff1c99eeb2df044ee3d4f0094eb033fee410
Serving image	ghcr.io/miaai-lab/glm-5.3-flash-2x-dgx-sparks@sha256:9bb1557a4234fce63d59599e44d10747eabd742beb337eebf9e7070be8a0fd58
Base model	zai-org/GLM-5.3-Flash (Z.AI)

How it compares with what was already public

The comparison that matters for a Spark owner is against recipes that were publicly available before this release. The reference rows below are author-reported: measured by each recipe's own author, on that author's hardware, with that author's harness. The JSpark3 v1 row is our own local measurement, and the Basis column states the conditions of every row. Rows without a matched local protocol remain context because node counts, quantization lanes, speculation, context, clocking, safety envelope, and estimators differ. The headline exceptions use the same frozen screen, the same sparkDash author protocol, or the same agent task and prompt, with each remaining mismatch stated in the linked benchmark page.

Recipe	Nodes	Lane	Context	Decode (tok/s)	Basis
JSpark3 v1 v1.0.0	3	EXL3/TR3 4 bpw, DFlash2, W8A16 trunk overlay	1,000,000	structured count 81.962; code 66.257; prose 29.049	local; frozen 24-request screen, thinking off, temperature 0, 400 max tokens, warm server; medians of three batteries; per-stream estimator
FlyCockpit TP3 9093765c	3	EXL3/TR3 4 bpw, DFlash2	1,000,000	structured count 69.0 / 68.5 / 71.2; code 52.3 / 58.7 / 58.2	author-reported
neko-legends TP4	4	EXL3/TR3 4 bpw, DFlash2	1,000,000	code 64.5; structured 100.9; math 77.8; prose 23.1; C4 aggregate 253	author-reported; four DGX Sparks, warm client-wall bench_exl3.py, thinking off
Mia TP2 c190db1a	2	EXL3/TR3 4 bpw, DFlash2	1,000,000	sparkDash C1 62.9; lab structured 65.1; lab prose 27.1	author-reported
jetnet TP3 bfc820ec	3	NVFP4 with Marlin W4A16, MTP-4	512K	35.2; DFlash2 lane, thinking on, 47.2	author-reported

Three of those recipes were also run on this fleet, each with a pinned source revision and every adaptation disclosed. None is an exact reproduction and none replays a source's own published harness, so these are separate evidence rather than a restatement of the rows above. Their same-task agent runs used independent trajectories, so the rates compare achieved product throughput rather than isolate an engine-only effect.

Same agent task	Fidelity	Nodes	Agent aggregate decode (tok/s)
JSpark3 v1	this release	3	44.583
mia-tp2-historical-0e2e78f	site/safety-adapted	2	24.913
mia-tp2-current-c190db1a-adapted	compatibility-adapted	2	24.728
fly-derived-9093765c-adapted	minimal-correctness/safety-adapted	3	29.042

On this task, JSpark3 delivered 1.8x the aggregate decode throughput of the current adapted two-Spark run, 44.583 versus 24.728 tok/s. The agents followed independent trajectories.

No literal FlyCockpit run and no jetnet run exists here; jetnet was studied statically and never run on this fleet.

What the overlay changed, internally

Separately from the comparison above, the project ran a matched A/B against the matched three-Spark control (same recipe, overlay disabled), an unreleased internal development build. That control is not a product, was never published, and is not a market comparison. It is the only comparison in this release where hardware, topology, checkpoint, draft, image, serving envelope, workload, estimator, and safety contract are all matched.

Hardware: three DGX Sparks (GB10, SM 12.1), two RoCE-v2 legs per node at MTU 9000. Server warm. Single-stream decode on a frozen 24-request plan (4 warm-up, 20 scored), thinking disabled, temperature 0, top-p 1, fixed seed, 400 max tokens; per-request rate is (completion tokens minus one) over the interval between the first and last visible streamed token, and the phase value is the median. All numbers are in RESULTS.json with their estimators.

Phase	Earlier control battery	JSpark3 v1 (median of 3)	Delta	Same-day paired control	JSpark3 v1 r3	Paired delta
Code	63.861	66.257	+3.75%	61.768	66.257	+7.27%
Structured count	77.510	81.962	+5.74%	76.863	81.962	+6.63%
Prose	28.308	29.049	+2.62%	26.810	29.049	+8.35%
C3 per-stream median	67.591	69.634	+3.02%	65.208	51.382	-21.20%
C6 per-stream median	37.460	54.694	+46.01%	53.149	54.694	+2.91%

The C6 gain against the older control battery mostly reflects that battery's state; the paired +2.91% is the credible figure. C3 was variable and lost its strict pairing.

Token pacing in the paired battery: median inter-token interval 98.645 to 91.912 ms (-6.83%), p99 120.472 to 108.105 ms (-10.27%), worst interval 364.416 to 148.344 ms (-59.29%).

Matched concurrency waves (aggregate service throughput, one wave each, 84 requests, thinking disabled): C12 155.733 to 155.986 tok/s (+0.16%), C24 206.235 to 208.723 (+1.21%), C48 229.966 to 237.946 (+3.47%). DFlash2 acceptance 64.323%, 65.935%, 64.562%. Fairness did not improve; C48 time to first token at p90 was 96.722 s.

Matched 113,908-token prefill proxy: 1277.443 to 1234.246 tok/s (-3.38%); time to first token 89.169 to 92.290 s (+3.50%). A measured regression.

Internal promotion gates the measured build missed, kept as disclosed evidence: campaign code median 66.257 tok/s against a 67.0 floor (short by 0.743 tok/s, 1.11%), and a longest uncompensated interior slow run of 14 against a limit below 5 in the agent demonstration. Neither is a correctness or stability failure.

Evidence grade for everything measured here: ENGINEERING-EVIDENCE, produced by the project on its own fleet; no third-party reproduction yet.

Intended use

Serving GLM-5.3 Flash on a three-DGX-Spark fleet you operate, for research, evaluation, and internal use consistent with the upstream licenses. JSpark3 is a serving recipe, not a model and not a fine-tune; the weights here are a mirror of someone else's quantization.

Limitations

Exactly three DGX Sparks; every input pinned; prefill slower than the matched control; three-stream waves variable; long time to first token at 48 streams; single-fleet evidence with small sample sizes; the sparkDash comparison uses the same author protocol but separate fleets and dates; the agent comparison uses independent trajectories; no literal FlyCockpit or jetnet reproduction and no jetnet run at all; no public accuracy benchmark for this release; no authentication on the endpoint. The full list is in the GitHub repository's docs/LIMITATIONS.md.

Licenses

The weights in this repository are licensed under the ShapleyMcg License v1.0, reproduced in full as LICENSE. It is a source-available, attribution-required license; it is not OSI-approved open source, and the license text says so itself. Downstream copies of the Work stay under it, and the license contains a named exclusion, reproduced as written in the license file. Attribution is a condition of the grant, not a courtesy.

The base model zai-org/GLM-5.3-Flash is MIT, Copyright (c) 2026 Z.AI Co., Ltd.; keep that notice with any copy of the base work. Third-party notices carried by the upstream repository are in THIRD_PARTY_NOTICES.md and THIRD_PARTY_LICENSES/.

JSpark3's own recipe code, tooling, and prose are Apache-2.0, in jspark3/RECIPE-LICENSE with its notices in jspark3/THIRD_PARTY_NOTICES.md. That license covers none of the weights. The DFlash2 draft is CC BY-NC-ND 4.0 for research and evaluation use; commercial use requires separate permission from Inco AI. The assembled endpoint is therefore neither unrestricted open source nor commercial-ready.

Attribution

This work includes or was produced using ShapleyMcg, created by Brandon M. Music (https://github.com/brandonmmusic-max/shapleymcg). ShapleyMcg is licensed under the ShapleyMcg License v1.0, an attribution-required license that grants no rights to the person known as "0xSero." Use of ShapleyMcg without this attribution is unlicensed.

DFlash2 is non-commercial research and evaluation use only absent separate permission from Inco AI. Apache-2.0 covers only this package's own code and prose.

@misc{music2026shapleymcg,
  author = {Music, Brandon M.},
  title  = {ShapleyMCG: An Auditable Calibration-to-Encoding Pipeline for
            Low-Bit Mixture-of-Experts Models},
  year   = {2026},
  url    = {https://github.com/brandonmmusic-max/shapleymcg},
  note   = {Licensed under the ShapleyMcg License v1.0}
}

Citation

@software{jspark3v1_2026,
  author  = {{JSpark3 authors}},
  title   = {JSpark3 v1: a reproducible three-DGX-Spark serving recipe for GLM-5.3 Flash},
  version = {1.0.0},
  year    = {2026},
  url     = {https://github.com/jakejharris/jspark3}
}

Cite the upstream works alongside it: Z.AI (GLM-5.3 Flash), Brandon M. Music (ShapleyMcg), Inco AI (DFlash2), z-lab (DFlash), MiaAI-Lab, FlyCockpit, vcruz305, vLLM, and ExLlamaV3.