JSpark3 v1.1 “Cadence”
Three DGX Sparks. One OpenAI-compatible GLM-5.3 Flash endpoint. One million tokens of context. Every number reproducible.
JSpark3 is the measured three-Spark path for the EXL3/TR3 checkpoint in this repository: the exact mirrored weights, a pinned serving stack, and a fail-closed fleet controller. v1.1 (Cadence) adds a measured single-stream decode mode and carries the proven long-context kernel fix. The weights are unchanged.
The first four tiles are JSpark3 v1.0.0 measured on this fleet against the two-Spark recipe. The Cadence effect is a v1.1 paired measurement against the recipe's own disabled reference route. Full conditions, CIs, and caveats: GitHub benchmarks page.
The fastest published Spark recipe for GLM-5.3 Flash
Single-stream decode, tok/s. JSpark3 is our own local measurement; every other row is author-reported on that author's hardware and harness, so treat them as context rather than a matched race.
Three Sparks running JSpark3 out-decode the four-Spark author-reported figure on code and on prose. Against the other three-Spark recipe, JSpark3 is 13 to 27% faster on code and 15 to 20% faster on structured output.
What v1.1 Cadence adds
- Single-stream decode mode: a QKV INT8 decode shadow plus a request-local speculative width controller. Batches of two or more requests and prefill fall back to the wide path automatically.
- Measured, not claimed: +19.17% prose and +7.61% structured-count decode in the second serving start, whose predeclared sham control passes. Code moved +3.23% with an interval spanning zero, so we do not call it a replicated gain.
- Long context that actually runs: the persistent_topk kernel disable now lives in the transform contract, so single-stream requests past 32,768 tokens no longer crash. Live verification returns the codeword verbatim at 48,957 prompt tokens.
- Same weights, same hashes: nothing here was re-quantized or re-uploaded. The card, the recipe tag, and the evidence changed. The bytes did not.
Full v1.1 paired results (both serving starts)
The v1.1 effects were measured inside the candidate against its own disabled reference route. They are not comparisons against the v1.0.0 numbers, which used different request sets and estimators. The first start's sham control failed its predeclared resolution margin, so those figures are diagnostic only.
Quality results, including candidate-only delivered-answer failures, and every caveat are in the GitHub benchmarks page.
v1.0.0 historical benchmarks (unchanged)
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.
v1.1.0 released 2026-09-07. The GitHub tag selects the release content; this card update is a documentation commit and changes no checkpoint or tokenizer bytes.
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, a selective W8A16 trunk overlay, and, in v1.1, the measured QKV decode shadow and speculative width controller.
- 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.
Known issues in the v1.0.0 recipe (historical)
Two issues affected the v1.0.0 recipe. Both were found by a community bug report from @BTCXoomer on X, both were fixed since v1.0.1, and v1.1.0 carries both fixes. The weights in this repository are not affected and nothing here was re-uploaded.
- Single-stream requests past 32,768 tokens crash the v1.0.0 recipe.
The v1.0.0 transform contract pinned vLLM's
persistent_topkkernel enabled, and on GB10 that kernel aborts once a single decoding stream passes 32,768 tokens of context (85 cooperative CTAs against the 48 the device holds residently; the fallback needs more shared memory than a GB10 exposes). Every measured run and every published benchmark executed with that kernel disabled, so the v1.0.0 construction had never actually been run past 32,768 tokens by anyone. v1.0.1 moved the disable into the transform itself, and v1.1.0 carries it in its transform contract; the transform emits the exact kernel file the measured arms executed. Integrated live verification now passes a pinned single-stream request with 48,957 prompt tokens and 51 completion tokens, returning the codeword verbatim. Candidatea729583and host verifier456a262are recorded separately in the release guide. The final archive has not been cold-boot tested, and maximum-context capacity remains unverified. No weight byte or historical benchmark figure changes. Details and v1.0.0 workarounds: the install-path warning and the mechanism and arithmetic. - Containers launched by hand need
NCCL_IB_SUBNET_AWARE_ROUTING=1. The variable is new in NCCL 2.30.7 and defaults to off; with it off, NCCL pairs NICs by index and routes rank 0 to rank 2 over rank 1's leg, which breaks a switchless three-node triangle. The lifecycle controller always set it; the v1.0.0 documentation never named it, so an operator launching containers with their own tooling could not know it existed. The v1.0.1 install steps list the full fabric environment.
Use the v1.1.0 release from the GitHub repository; install commands select that exact tag. Benchmarks are unchanged across the kernel fix: every published number was measured with the kernel disable applied, which is the same file the transform now emits.
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 verified weights-mirror revision. The
recipe, documentation, and evidence are in the
v1.0.0 GitHub release,
released 2026-09-02, preserved as history. The current recipe is
v1.1.0 (Cadence), dated 2026-09-07.
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.jsonwere remotely verified before merge into the verified weights-mirror revision. Seejspark3/UPLOAD.mdfor 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
This section and the one after it are the v1.0.0 historical record, preserved unchanged; the v1.1 Cadence evidence is never merged with these tables.
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 | v1.0.0 (historical) | 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
This is v1.0.0-era evidence about the trunk overlay, preserved unchanged; the v1.1 additions have their own paired evidence in the GitHub benchmarks page.
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 the v1.0.0 record 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; the v1.1 gains are single-
stream-scoped, with batches falling back wide and no replicated code gain;
first-start v1.1 figures are diagnostic only because that start's sham
control failed its predeclared resolution margin (the second start's
predeclared sham passes);
quality contains candidate-only delivered-answer failures and inconclusive
population parity; the concurrency burst evidence certifies no sustained
service, fairness, or capacity envelope; 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 kernel-file transform change is verified
and integrated live verification passed one request with 48,957 prompt
tokens and 51 completion tokens; maximum-context capacity, sustained
concurrency, and final-archive cold-start validation remain unproven.
The focused witness is a fixed admission workload, not a Pi speed claim;
interactive performance remains workload-dependent. The v1.0.0 recipe
carried the two known issues described above, fixed since v1.0.1. 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{jspark3v11_2026,
author = {{JSpark3 authors}},
title = {JSpark3 v1.1 (Cadence): a reproducible three-DGX-Spark serving recipe for GLM-5.3 Flash},
version = {1.1.0},
year = {2026},
url = {https://github.com/jakejharris/jspark3/releases/tag/v1.1.0}
}
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.
- Downloads last month
- 142
Model tree for jakejharris/jspark3
Base model
zai-org/GLM-5.3-Flash