♡  a fork of blaesus/dsgx10 that lets many people share one 4-Spark DeepSeek  ♡
     faster single-user  ·  2x+ the multi-user throughput  ·  1M context  ·  images

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✧ the numbers

┌──────────────────────────┬──────────────┬──────────────┬────────────┐
│ metric                   │ upstream     │ pico-dgx-ds4 │ sglang tp4 │
├──────────────────────────┼──────────────┼──────────────┼────────────┤
│ chat, 1 user, 320 prompts│ -            │ 90.3 (86.2)  │ -          │
│ chat, 1 user, 3 prompts  │ 80           │ 85           │ 64         │
│ code, 1 user, 3 prompts  │ 140          │ 144          │ 117        │
│ code, 1 user, 320 prompts│ -            │ 133.6 (128.2)│ -          │
│ 8 users, distinct        │ -            │ 307          │ -          │
│ 16 users, distinct       │ -            │ 376          │ -          │
│ 16 users, copies         │ 177          │ 441          │ 427        │
│ agents (8 x 80-300K ctx) │ ~150         │ 260-270      │ -          │
│ prefill                  │ 10k          │ 10k          │ 5.3k       │
│ max context              │ 256K         │ 1M           │ 1M         │
│ images                   │ no           │ yes          │ -          │
│ quality (qeval /75)      │ 71           │ 71-72        │ 70         │
└──────────────────────────┴──────────────┴──────────────┴────────────┘
  tok/s · 4x DGX Spark, TP4 over a 200G RoCE switch · temp 0 · (in brackets: before stack4 + stack5)
  Headline single-user: 90 chat (mean over 320 held-out prompts) / 144 code (3-prompt bench).
  distinct = a different prompt per user; copies = prompts repeated (shares experts
  and drafts, ~15% higher). Upstream and sglang were only measured with copies.
  All numbers are our own bench, not sparkDash.
  Single-user = 27-run medians of our bench on the live build, 2026-11-11: 84.6 / 144.0
    (2026-11-10, before stack5: 82.7 / 142.6; the medians swing by ±3 between runs). The
    paired rows are the proof: the same 320 held-out prompts, same texts: chat 86.2 exact
    build -> 89.5 with stack4 -> 90.3 with stack5 (89.2 without it, same binary, same hour).
  On sparkDash 1.8.8's method (live build, 2026-11-11): prose 102.0, code 164.6; catalogs
    80.7 / 132.4 / 156.0 (before stack5, same binary, same hour: 100.6 / 163.0;
    78.8 / 128.8 / 154.1). Upstream README: 95 / 151.
  16 users = medians of 3 runs on the live build, 2026-11-11 (distinct 374 / 376 / 378,
    copies 441 / 448 / 439). 8 users and agents: gate runs on a test port, same build.
    Reproduce the bench rows with tools/bench/bench.py.
  A lossy relaxed-accept experiment (103 / 163) was tried and removed.

✧ what changed

  1M context ......... 17-bit block index in the sparse-attention indexer
  big prompts ........ front-end body limit 2 MB -> 64 MB
  16 slots ........... 4 hold 1M, 12 hold 128K, smallest-fit admission
  16-row GEMVs ....... norm once, then the plain r16 GEMV (same bits)
  draft budget ....... DSGB10_BUDGET=1, drafts shared across sessions (lossless)
  expert re-route .... DSGB10_OEA=K0,KP,MIN, opt-in, batch-aware MoE (lossy, qeval-gated)
  wide step .......... DSGB10_WIDE=64, steps of 17-64 rows via prefill GEMMs + moe_prefill
  1-pass drafter ..... DSGB10_DRAFT1=6, every session drafts in one pass
  cost gate .......... DSGB10_WIDE_COST, wide only when the drafts pay for it
  vision ............. image_url / base64 images via the checkpoint's own ViT (side encoder)
  4-bit draft head ... --draft-head4 --draft-markov8 + DSGB10_HEAD4_C1=1 (one-user drafts only)
  learned gate ....... DSGB10_CF_MLP=FILE, a small MLP decides how many drafts a step verifies
  certified head ..... DSGB10_CERT=1, greedy steps read a coarse head + only the rows that can win
  direct ring ........ DSGB10_RING_DIRECT=2, on a switch each rank writes to every rank itself (no relays)
  (removed) relaxed accept: lossy single-user experiment, not part of the build (CHANGES.md #12)

Per-commit details and measured before/after numbers are in CHANGES.md; the design is in docs/wide64.md.

✧ stack5: sync without relays

Upstream's collectives were built for a cabled ring: each box sends to its two neighbours and they relay to the box opposite. On a switch that second hop is not needed. With DSGB10_RING_DIRECT=2 every box writes to all three others itself; buffers, flags and the order of every sum stay the same, so the results are the same bits. A 10 KB all-reduce went 14.5 -> 9.9 us, the engine's step 0.2-0.6 ms shorter: chat +1.3 %, code +1.8 %, math +1.8 % on the 320 held-out prompts, same texts; 16 users with different prompts 364 -> 373 in the same window (live medians 368 -> 376). Two other levers were measured and not built: losslessly coded FP8 weights decoded inside the GEMV (about par with today's kernels, Huffman slower) and a 4-bit drafter body (the lost drafts cancel the shorter pass). The 100 tok/s chat target was not reached. Details: docs/stack5.md.

✧ stack4: three small single-user levers

Three small changes for one user, stacked: chat +3.8 %, code +2.1 %, math +2.5 % on 320 held-out prompts, with the same texts. Details, proofs and what was measured but not built: docs/stack4.md.

  1  4-bit draft head ...... the drafter's vocabulary head as MXFP4 and its Markov head as FP8
                             (upstream's formats) for one-user drafts; multi-user passes keep
                             the FP8 copy. Draft pass -0.3 ms. Drafts only propose.     +1.1 %
  2  learned verify gate ... 15 numbers the draft pass already has (per depth: the shipped
                             confidence, the top-1 margin, the top-1 log-probability) go
                             through a 64-unit MLP that decides how many drafts to verify.
                             Accepted drafts per step +5 % on chat.                     +1.1 %
  3  certified LM head ..... greedy steps read the FP8 copy of the head for all rows, bound
                             each row's error rigorously, and run the exact kernel only on
                             rows that could still win. Same token, proven on 1.34 M real
                             step rows with the full head beside it: 0 mismatches.      +1.5 %
     (gains: chat tok/s on the 320 held prompts, each on top of the one before)

Honest note on "same output". Levers 1 and 3 keep every verify step as it was. Lever 2 changes which drafts get verified. Every token is still the full model's own top choice in the step that produced it, but this engine's logits depend slightly on how positions are grouped into steps, so a different grouping can flip a close call. With the shipped setting all 374 test outputs are identical to the exact build's; with a looser setting (alpha 0.9) one of 320 changed by a word. The same effect already exists whenever another request shares a step.

Ideas behind these, with thanks: DeepSeek's DSpark (arXiv 2607.05147), Carryover Drafting (arXiv 2609.14717), certhead (github.com/nickharris808/certhead) and CSV-Decode (arXiv 2511.21702), FR-Spec (arXiv 2502.14856), TVP (arXiv 2611.00588).

✧ vision

Images work through the normal OpenAI API (image_url with a data: URL or an http(s) URL, several per request). The checkpoint's own ViT + aligner runs as a small side encoder (~1 GB of GPU memory on rank 0), and the engine puts its rows in place of the image tokens during prefill only. Text requests take exactly the old path.

┌──────────────────────────────────┬────────────────────────────┐
│ image checks (temp 0)            │ 15/15 (10 known answers,   │
│                                  │ 2-image, image + 28K text, │
│                                  │ http URL, bad image = 400) │
│ TTFT added per image             │ ~0.17-0.47 s (median ~0.26)│
│ encoder, warm                    │ 100-160 ms / image         │
│ text after: chat / code c1       │ 81.9 / 144.4 (was 79.7/138)│
│ text after: 16 distinct users    │ 380 (was 367)              │
│ text after: agents, qeval        │ 266 (260), 71/72 (71/71)   │
└──────────────────────────────────┴────────────────────────────┘
  differences in text numbers are run-to-run noise; images 214-460 tokens each

Design, protocol and the full numbers: docs/vision.md.

✧ run it

This is the exact build behind the numbers above (the engine source in this repo is the source of the binary we serve).

  1  hardware ....... 4x DGX Spark (GB10, 128 GB), ConnectX-7 links. We run TP4 through a 200G RoCE
                      switch; upstream's switchless ring is the other supported cabling.
  2  base setup ..... follow ds41f-4xgx10-switchless/README.md "How to use" (build, fleet file,
                      weights, Engram shards). Build from THIS repo, not upstream:
                        cd ds41f-4xgx10-switchless
                        cmake -S engine -B engine/build && cmake --build engine/build -j4
                        (cd frontend && cargo build --release -j4)
  3  gate weights ... copy ds41f-4xgx10-switchless/tools/stack4/cfmlp_cml.bin to the SAME path on
                      all four boxes (e.g. ~/cfmlp_cml.bin). The engine exits at start without it.
  4  engine args .... the block below, through service.sh
  5  images ......... optional: start tools/vision/vision_encoder.py on rank 0 and add
                      --vision-addr 127.0.0.1:18950 to the front-end's arguments (docs/vision.md)
  6  check .......... python3 tools/bench/bench.py http://127.0.0.1:PORT/v1/chat/completions c1 27
                      python3 tools/bench/bench.py http://127.0.0.1:PORT/v1/chat/completions distinct 16

DSGB10_ENGINE_ARGS="--max-ctx 1048576 --slots 16 --big-slots 4 \
  --setenv DSGB10_BUDGET=1 --setenv DSGB10_OEA=4,12,9 \
  --setenv DSGB10_DRAFT1=6 --setenv DSGB10_WIDE=64 \
  --setenv DSGB10_WIDE_COST=24,2.2,57,1.1 \
  --draft-head4 --draft-markov8 --setenv DSGB10_HEAD4_C1=1 \
  --setenv DSGB10_CF_MLP=~/cfmlp_cml.bin --setenv DSGB10_CF_ALPHA=1.0 \
  --setenv DSGB10_CERT=1 --setenv DSGB10_RING_DIRECT=2"
# DSGB10_RING_DIRECT=2 only when all four boxes are on one switch (every rank reaches every
# rank in one hop). On upstream's cabled ring leave it out.

What to expect on that bench with nothing else on the endpoint: chat about 80-85 and code about 140-145 for one user (chat runs swing between ~75 and ~95 by prompt), about 376 tok/s for 16 users with different prompts, about 441 with repeated prompts. The 90.3 chat figure is the mean over our 320 held-out prompts, a different and larger prompt set. The confidence-gate weights were fitted on this checkpoint (DeepSeek V4.1 Flash, 1017 build); on another checkpoint the output stays the model's own, but the gain may differ.

Drop DSGB10_OEA for a strictly lossless multi-user build (details: docs/c1fast.md).

✧ credits

  engine, kernels, RoCE ring, drafter ...... Andy Shu, blaesus/dsgx10 (MIT)  ♡ thank you
  multi-user, 1M context, wide step ........ Pico (Patrick Moorhead), this fork
  model, DSpark drafter .................... DeepSeek
  stack4 ideas ............................. Carryover Drafting, certhead, CSV-Decode, FR-Spec, TVP (see above)

The upstream LICENSE and NOTICE are unchanged and cover this fork. Upstream's README follows.

upstream README (blaesus/dsgx10)

dsgx10

From-scratch inference engines for NVIDIA GB10 boxes. Each directory is one configuration — a model, a set of boxes, a topology — and is specialised to it: speed over generality. Each is self-contained, with its own engine, front-end, docs and tools, and its README is its index.

Configuration	Model	Boxes and topology	State
ds41f-4xgx10-switchless (how to use)	DeepSeek-V4.1-Flash, built on the community variant dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8 at d61c59ea (the official architecture and tensor formats; the values of shards 13–39 differ; hence the served name deepseek-v4.1-flash-uncensored); measured since 2026-10-13 on deepseek-ai's official checkpoint (DeepSeek-V4.1-Flash at fb2764a5)	four GB10 boxes (two ASUS Ascent GX10 and two NVIDIA DGX Spark, ranks 0–3) in a switchless ConnectX-7 ring, TP=4	serving the variant on rank 0:8888 from 2026-10-12, and DeepSeek-V4.1-Flash-1017 since 2026-10-18 (dsfront --encoding 1017). On the official checkpoint: single-stream decode 77–154 tok/s with speculative drafts (the SGLang kit on it the same day: 51–108), cold prefill 8.5–9.2k tok/s at 16k–57k tokens (kit: 4.3k) and 9.4–9.5k at 120k; on the variant, agentic turns flat to 250k tokens of context
altar1-4xgx10-switchless	AikidoSec/altar-1 at 5d591cd9 (GLM-5.3 pruned to 168 of 256 routed experts, compressed-tensors INT4 g32 experts and attention projections, the rest BF16; nothing requantised)	the same four boxes, TP=4	Serving (2026-10-14): A0–A8 done on the four installed rank files, within the reference's own order variance on all four golden sets, bit-stable, 45.7 ms a step at one row (1.23× the byte floor), drafts exact at 28–30 tok/s, the service handing the ring over with the DeepSeek service → where it stands

ds41f-4xgx10-switchless: where it stands (2026-10-13)

On deepseek-ai's official checkpoint (DeepSeek-V4.1-Flash at fb2764a5), with release candidate 5's engine code (built from 530af7f), each figure measured with its original tool, prompts and settings. Production serves the community variant the engine was built on; on it, release candidate 5 decoded 75.7 / 111.7 / 109.2 / 149.8 tok/s on bench.py the same morning, at the same prefill and agentic times (history).

Against the SGLang TP=4 kit that served on this ring before (MiaAI-Lab/DeepSeek-v4.1-Flash-DGX-Sparks at cad252b, v2.1, over NCCL), on the same checkpoint and ring the same day, with the same benchmark (bench/bench.py: speculative drafts on both, greedy, 512 tokens, one request at a time):

	dsgb10	SGLang kit
decode tok/s: prose / code / agentic / JSON	77.4 / 115.4 / 102.9 / 153.7	50.9 / 85.6 / 76.3 / 107.7
time to first token, short prompts	124–156 ms	219–253 ms
cold prefill, 16k–57k tokens	8.5k–9.2k tok/s	4.3k tok/s
agentic: first turn (15.8k tokens)	1.81 s	3.72 s
agentic: turns adding ~2k tokens, to first token	0.39–0.42 s	0.68–0.79 s

The table compares dsgb10 with the MiaAI kit's TP4 line on a switched RoCE fabric (knapcio/DeepSeek-V4.1-Flash-4x-DGX-Spark-TP4, v2.4 of 2026-10-12 unless marked), whose decode figures were the best published for four GB10s at the checkpoint's native precision on 2026-10-13; both run deepseek-ai's official checkpoint. It uses that project's own benchmark (sparkDash 1.8.8: its prompts, 256 tokens, greedy, one stream). No published native-precision four-GB10 figure we found beats dsgb10's on any row (survey); the nearest is a code step 1.7 % shorter than ours that carries a third fewer tokens. The best per c1 type are 90.2 / 133.2 (v2.2) / 158.7 / 135.3 (a community ring run of v2.3), and the best real-text prefill is 5.7k–5.9k (christopherowen's spark-ds41f, vLLM). A 2.9-bpw quantisation publishes higher code and counting rates on its own prompts.

	dsgb10	MiaAI kit
decode tok/s: prose / code / structured / JSON	95.0 / 151.0 / 185.8 / 150.2	89.9 / 131.0 / 158.7 / 128.4 (v2.4)
sparkDash's prose / structural catalogs, one stream (ours: 256 tokens each, the mean; the kit's: its own harness)	78.1 / 122.3	61.1 / 99.0 (v2.2)
a decode step, prose: ours on sparkDash's prompt, the kit's on its own probe prompts	27.9 ms at 2.63 tokens	31.2–32.5 ms at 2.27 (v2.2)
a decode step, code: likewise	34.8 ms at 5.20 tokens	37.4–37.8 ms at 3.87 (v2.2)
cold prefill, real text, 15k–120k tokens	8.7k–9.5k tok/s	4.8k–5.0k tok/s (v2.1; the best published, spark-ds41f's: 5.7k–5.9k)

As the coding agent pi uses it, on the agentic-edit bench (twelve pi-style coding tasks, three times, at dsfront's sampling defaults, thinking off): 138.5 and 134.2 s end to end, 110.4 and 111.9 output tokens a second of it, decoding at 137.4 and 138.5 tok/s, with 229–230 ms to the first token (median). All 36 task checks passed in both runs.

On the same checkpoint, dsgb10 answers 99 of GSM8K's first 100 test problems (the SGLang kit, the same day: 100), finds the passkey in 18 of 18 prompts of 33k–220k tokens, and, teacher-forced along the kit's greedy outputs on four prompts, picks the kit's token at 260 of 271 steps.

How it was measured:

• Each dsgb10 figure is the mean of its sessions on 2026-10-13: six of bench.py (three before the kit's sessions, three after: O2 / O1 0.997–1.004 on decode), two of sparkDash, two of the edit bench (its end-to-end times are given for each). Each session was a fresh engine at production's arguments on the official files; other agents' short jobs held the ring between sessions, never during one. bench.py's sessions agreed within 0.8 % on decode (their requests within 1.3 %), sparkDash's within 0.2 %. Each side's time to first token leaves out each session's first request (ours 164–168 ms, after an engine start; the kit's 215–218 ms).
• The SGLang kit was measured on this ring on 2026-10-13 from 11:13 to 11:32, in one start: a warm-up session, left out, then three sessions, with at most one request running and none queued in its own log. Each decode figure is the mean of three session means, its requests 0.7–2.2 % apart; each prefill figure and the first agentic turn the mean of three requests. It was set up to batch up to 16 requests; every figure here is one request at a time, so throughput under concurrency is not compared. On the variant, on 2026-10-11, it decoded 46.8 / 77.4 / 73.6 / 106.9 tok/s (its baseline).
• The MiaAI kit's figures are from its own documents, not measured here. A stack that sums in another order follows another greedy text on the same prompt, so the single-prompt rows compare slightly different texts. The kit's documents recommend comparing step times and many-prompt runs across stacks, but its step probe and its catalog harness are its own and unpublished, and its steps carry fewer tokens, so no decode row is like for like; the prefill row is the one least affected.
• Against the variant, a step costs the same on the official checkpoint (prefill, the agentic turns and decode without drafts within 0.7 %); decode with drafts follows the drafts' acceptance on each checkpoint's greedy texts: −5.7 to +3.4 % on bench.py, −7.6 to +2.0 % on sparkDash's single prompts.
• The write-up, with every session's figures, the kit's conditions, the correctness checks and how the runs differ from release candidate 5's: docs/engine.md. Every experiment, failed ones included, is indexed in docs/experiments.md. The raw data and the scripts are in rank 0's /data/dsgb10/bench/wave2 and rank 0's /data/dsgb10/bench/official.

Where to read, inside a configuration:

• README.md: where it stands, the status of every part, the layout, how to build and run;
• DESIGN.md: the decisions, budgets and milestones;
• docs/: what was measured and learned, with the evidence.

Large data (weights, golden activations, logs) never goes in the repo; each configuration's README says where it lives on the boxes.

Licence

The code is under the MIT licence (the LICENSE file). ds41f-4xgx10-switchless/ref/inference and ref/encoding are DeepSeek's reference code under their MIT licence (ref/LICENSE-deepseek); the agentic-edit bench carries the coding agent pi's prompt and tools (MIT, bench/THIRD_PARTY.md); other third-party material is listed in NOTICE. No model weights are included.

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