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Fresh prompts over --short-read decode !!!! (token 0) when the GPU holds 100% of the experts (48 GB card) #871

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Description

@etzerg

Fresh prompts over --short-read decode !!!! (token 0) when the GPU holds 100% of the experts (48 GB card)

Summary

On a card large enough to hold every expert of IQ2_XS in VRAM, any fresh prompt whose new text exceeds
--short-read (default 64 tokens) answers a run of ! (token 0). Shorter prompts, and requests that hit the
prompt cache, answer normally. Capping the expert cache below 100% residency makes it go away with no other
change. This looks like the all-resident ("zero-doorbell", #646) path on the batched prompt read, the same
family as #792, which notes that BATCHING.md was measured only on cards below 100% residency. A 24 GB card never
reaches full residency with IQ2_XS, which would explain why this has not been reported.

Environment

  • Strata 6f32ec0, ready-made engine 0.1.39 (sm_75/86/89/120, CUDA 13.0), Windows 11 Pro 26200
  • GPU: RTX 4090 48 GB (memory-modded board), compute capability 8.9, driver 591.86
  • CPU: Intel i7-14700K (AVX2, no AVX-512, 8P+12E, --pool-workers 13 chosen by setup); RAM: 64 GB
  • Model: Qwen3.8-Flash-Next GSQ-RCO IQ2_XS (original family)

Reproduction (untouched install)

  1. Fresh clone, START-HERE.bat --yes (no other flags). Setup's settings:
    --expert-cache auto --prefill auto --spec 4 --spec-min-p 0.5 --max-context 131072 --kv int8 --kv-resident 32768 --pool-workers 13
  2. Startup log: filling the GPU's expert cache (24576 experts, 33.02 GiB of VRAM) /
    expert cache 24576 slots, 33.02 GiB of VRAM
  3. Send a fresh ~350-token user message (any content; a random prefix avoids the prompt cache),
    reasoning_effort: "none".

Result: 4/4 requests answer !!!!!!!!…. Sampling makes no difference (temperature 0, 0.7, absent).
The serve log for the bad requests shows every draft accepted:

strata serve: prompt 354 tokens = 0 reused + 354 read in 390 ms (907.6 tok/s), 40 generated in 219 ms (182.8 tok/s), drafts accepted 18 of 18, 1 checkpoints

What narrows it down

Threshold is exactly --short-read. Same prompt truncated word by word, fresh each time:

prompt tokens (incl. template) result
28, 35, 70 ok
72, 75, 76, 80, 92, 137, 237, 354 !!!!

The decode path is fine.

  • --short-read 1000000 (every fresh part through the decode windows): 0/3 bad, but a 40K prompt reads at 352 tok/s.
  • Repeating the same prompt (prompt cache hit, only the tail read through the decode path) answers correctly, even
    though the reused KV came from the batched read. So the KV written by the batched read looks fine; what breaks
    is the handoff from the batched read to the first decoded token.

It depends on residency, not on anything else. Same config, only --expert-cache changed, no other workaround:

--expert-cache slots the engine allocated fresh 354-token prompt
auto 24576 (100%) 4/4 bad
24000 24576 (rounded up to 100%) 4/4 bad
23000 24076 (98%) 0/4 bad
20000 20939 (85%) 0/4 bad

With 23000: 40K prompt 3,780 tok/s, decode 195–217 tok/s, which is what we run now.

Ruled out (still bad with each): low-RAM resident mode, --low-ram mmap, normal RAM mode; --vision on/off;
KV streaming on/off (--max-context 32768, no --kv-resident); --spec 2; --pcie-frac 0;
STRATA_RESIDENT_PIN=0, STRATA_ARGMAX_MULTI=0, STRATA_PF_FUSED=0, STRATA_PLE_BATCH=0.
CUDA_LAUNCH_BLOCKING=1 makes the engine exit with 0xC0000409 (stall dump written).

Two smaller points

  • --expert-cache 24000 rounds up to the full 24,576, so a user picking a "just under" value to avoid the
    all-resident path lands back on it. Some note or a hard cap below the total would help.
  • With a 48 GB card the default auto always fills the cache, so every fresh prompt over 64 tokens fails out of the
    box. Most clients (agents with a system prompt plus tool schemas) never send one under 64 tokens.

Happy to run a debug build or STRATA_* switches on this machine if useful.

Activity

  1. ischencheng commented on Oct 5, 2026

    @ischencheng
    Contributor

    I could get the same !!!! on an L40S (49140 MiB, same size as your card, IQ2_XS fully resident) once the residency table's copy to the GPU lands late, and #1001 makes the engine wait for that copy. On Linux it lands in time, so plain main was fine there, and I couldn't test Windows. If you can build from source, could you try #1001 with --expert-cache auto? It only changes src/program/generate.cpp.

  2. Niko1221 commented on Oct 6, 2026

    @Niko1221
    Owner

    Fixed in 0.1.40: the zero-doorbell graph is used only while every expert of the stage is in VRAM. A prompt loan (or a VRAM shrink, or a swap in flight) now makes the window run the normal doorbell graph, so it no longer plans from a table with -1 entries. The plan kernel also raises an error flag the host checks, so a stale plan fails the window instead of printing '!!!!'. The residency table uploads wait for their own copy too (PR #550). Please confirm on 0.1.40 at 100% residency with --short-read.

  3. added a commit that references this issue on Oct 7, 2026
  4. added a commit that references this issue on Oct 8, 2026
  5. etzerg commented on Oct 8, 2026

    @etzerg
    Author

    Confirmed fixed on 0.1.40.3 at 100% residency. Thanks!

    Setup: Strata d5ea713, ready-made engine 0.1.40.3 (CUDA 13.0), Windows 11 Pro 26200, RTX 4090 48 GB, driver 591.86. I updated with setup.py --update; my existing configs were left as they were. I tested the release rather than building #1001.

    What I changed: only --expert-cache 23000 back to --expert-cache auto. --short-read stays at the default 64. The log shows full residency again:

    strata generate: expert cache 24576 slots, 33.02 GiB of VRAM
    strata generate: session is up (engine 0.1.40.3)
    

    Results (fresh prompts with a random prefix so the prompt cache never hits, reasoning_effort: "none"):

    prompt tokens (incl. template) result
    41, 71, 93, 123, 173, 240, 443, 799 ok
    ~489, 4 runs 4/4 ok (was 4/4 !!!! on 0.1.39)
    14,428 – 29,545 (11 runs, --ple-io direct and mmap) all ok
    46,404 ok

    I have also been using it in a real agent loop since then (--max-context 524288 --rope-scaling yarn --rope-scale 2). One request was a 342,663-token fresh read. No !!!! so far.

    One note: I tested on Swift-1.5 IQ2_XS (UkisAI's fine-tune of Qwen3.8-Flash-Next). It has the same 24,576 experts / 33.02 GiB, so it takes the same all-resident path as the Flash-Next IQ2_XS I originally reported with. I haven't re-run the original Flash-Next IQ2_XS at auto yet. I'll add a comment if it behaves differently.

  6. added a commit that references this issue on Oct 26, 2026
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