Qwen3.8 base linear attention passes all 12 quality cases in NVFP4

All 180 base linear-attention projections pass 12 quality cases in NVFP4 with zero regressions and remove 2,997,226,080 bytes per c16 step.

Question

Can the whole base_linear_attention weight family serve as NVFP4 after its FP8 control passed?

setup	value
nodes	head GB10 and worker GB10, 6.17.13-rocket64k, 64 KiB pages
checkpoint	nvidia/Qwen3.8-Flash-Next-NVFP4 at fc694b54fb0174e0913e6adf86691ef85a4ead47
Rocket commit	9b0c50013077e8a27f8db9960f1cf1117070d68a
vLLM image	vllm/vllm-openai:qwen38-flash-next at sha256:d464f3b466fa9c45ddbff8a812e80564503b6879a9fd95c1a47514f3f0df5a4a
runtime source	MiaAI-Lab Qwen3.8 dual-Spark at c2325b22602b51a5faf55fc2bebccc34f3f80b9f
source weight dtype	BF16 for all 180 selected projections
serving weight dtype	ModelOpt W4A4 NVFP4 for all 180 selected projections
artifact	64539e4a...56c4e5

Prior absence was a difficulty constraint: this model needs a 180-tensor overlay plus a separate block-FP8 dispatch for its MTP experts on two GB10s.

Commands

scripts/numerics/qwen38-expanded-calibration.sh \
  --output-dir /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04 \
  --nvfp4-artifact-dir /home/glwillen/calibration/qwen38-linear-nvfp4-artifacts/64539e4a5a4b533aa143d20e055ae73ebd10b21f05dbd422847a9be30e56c4e5 \
  --launch

python3 scripts/numerics/qwen38-compare-precision.py \
  --baseline /home/glwillen/calibration/qwen38-expanded-mtp3-20260907-03/combined-activation-summary.json \
  --candidate /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/combined-activation-summary.json \
  --baseline-quality /home/glwillen/calibration/qwen38-baseline-quality.json \
  --candidate-quality /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/quality.json \
  --out /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/precision-comparison-bf16.json

python3 scripts/numerics/qwen38-compare-precision.py \
  --baseline /home/glwillen/calibration/qwen38-linear-fp8-live-20260907-08/combined-activation-summary.json \
  --candidate /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/combined-activation-summary.json \
  --baseline-quality /home/glwillen/calibration/qwen38-linear-fp8-live-20260907-08/quality.json \
  --candidate-quality /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/quality.json \
  --out /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/precision-comparison-fp8.json

channels    494
answer_tail_equal   10/12
regressions none
channels    494
answer_tail_equal   10/12
regressions none

Quality

metric	NVFP4	unit
quality gate	12/12	cases
byte-identical answer tails vs BF16	10/12	cases
byte-identical answer tails vs FP8	10/12	cases
regressions vs BF16	0	cases
regressions vs FP8	0	cases
paired telemetry	494	channels
changed tails vs BF16	arith_chain, negation	cases

Interaction telemetry

output family	channels	RMS ratio vs BF16	max histogram TV vs BF16	max histogram TV vs FP8
linear-attention projection output	108	0.703x to 2.370x	0.312500000	0.187500000
linear-attention output	36	0.703x to 1.311x	0.133300781	0.130859375
recurrent state	36	0.494x to 1.807x	0.111816406	0.083984375
full-attention QKV output	12	0.803x to 1.023x	0.071777344	0.112792969
full-attention output	12	0.924x to 1.244x	0.127441406	0.109863281
router logits output	48	0.893x to 1.014x	0.009765625	0.012695312

Residency and MTP

python3 scripts/numerics/qwen38-mtp-runtime-evidence.py \
  --log /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/logs/head-workload.log \
  --not-before 2026-09-07T07:51:54Z --min-records 2 --positions 3 \
  | jq '{records:.evidence.records, accepted:.totals.accepted_tokens, drafted:.totals.drafted_tokens, acceptance_rate:.totals.acceptance_rate, mean_length:.mean_acceptance_length}'

rg 'Model loading took' \
  /home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/logs/{head,worker}.log

{
  "records": 15,
  "accepted": 2458,
  "drafted": 4965,
  "acceptance_rate": 0.4950654582074522,
  "mean_length": {
    "max": 2.99,
    "mean": 2.493333333333333,
    "min": 2.11
  }
}
/home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/logs/worker.log:2026-09-07T07:49:34.101135822Z (Worker_TP1_EP1 pid=196) INFO 09-07 07:49:34 [model_runner.py:407] Model loading took 62.88 GiB memory and 192.568486 seconds
/home/glwillen/calibration/qwen38-linear-nvfp4-live-20260907-04/logs/head.log:2026-09-07T07:49:34.249269392Z (Worker_TP0_EP0 pid=253) INFO 09-07 07:49:34 [model_runner.py:407] Model loading took 62.88 GiB memory and 192.762584 seconds

metric	value	unit
model memory per node	62.88	GiB
accepted MTP tokens	2,458/4,965	tokens
MTP acceptance rate	49.51	%
mean accepted length	2.4933	tokens
mean-length range over 15 records	2.11 to 2.99	tokens
traffic removed per c16 step	2,997,226,080	bytes

One quality and telemetry run was measured. Model-memory spread was 0.00 GiB at two-decimal precision. MTP mean length ranged from 2.11 to 2.99 across 15 runtime records. Run-to-run variance was not measured.

Verdict

Accepted. All 180 base_linear_attention projections move from BF16 to NVFP4 in the serving-weight map. Existing routed experts stay NVFP4, and MTP experts stay 128x128 block FP8.

This vLLM measurement is a comparison baseline. The Rocket engine target stays above 20 generated tok/s per stream at concurrency 16 and above 320 aggregate tok/s.

Next

• run the healthy-clock production concurrency ladder with this NVFP4 artifact
• rank the remaining BF16 and FP8 families from measured c16 traffic
• retain family-isolated telemetry for each serving-weight conversion

Reopen if

• NVIDIA publishes a new checkpoint revision or ModelOpt NVFP4 serialization ABI
• an upstream evaluation release adds a different attention test set