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Two-Spark MoE: served-rate and warmup audit

Two-node serving study that distinguishes end-to-end served rate from decode rate, and audits cache, warmup and checkpoint identity.

Read the original source Publisher / project JSON ↗Markdown ↗
Source checked 2026-10-17 · Not independently reproduced by Run AI Here.

THE SOURCE IN CONTEXT

The report divides all generated tokens by the complete request wall clock, including first-token delay. Every current table request is forced to 512 tokens with ignore_eos. Earlier naturally stopped outputs and 64-token correctness probes have different denominators and remain in the historical report rather than being merged into this cohort.

What does served rate include?

The report divides all generated tokens by the complete request wall clock, including first-token delay. Every current table request is forced to 512 tokens with ignore_eos. Earlier naturally stopped outputs and 64-token correctness probes have different denominators and remain in the historical report rather than being merged into this cohort.

Source: WeZZard · pinned document

Why were earlier results replaced?

The author remeasured the campaign on 5 September after discovering a slow-clock GPU and unequal prefix-cache policy. The corrected runs disable prefix caching for all three models, exercise complete workload shapes during warmup and retain per-node JIT caches. Earlier measurements remain visible as history; they should not be treated as the corrected cohort.

Source: WeZZard · pinned document

What can the long-context rows establish?

At one user in the long agentic workload, the reported medians are 22.5, 21.0 and 17.7 served tokens/s, with different between-campaign spreads. The author labels this row not separated by the data. Different draft models and KV pools remain attached to each model, and the high-concurrency rows do not even use the same number of users across models.

Source: WeZZard · pinned document

Why are failures and checkpoint identity useful?

The README reports a Qwen FP8-KV configuration that failed on chunked input and GLM backend attempts that did not complete a forward pass. Its DeepSeek current rows use the text 0731 checkpoint, not Vision-Exp and not V4.1 Flash 1017. These failures and identities explain why a recipe label cannot substitute for the actual six-field serving configuration.

Source: WeZZard · pinned document

Reported figures and their conditions

Selected facts reported by the original author. Each row has its own configuration and measurement boundary. Run AI Here has not reproduced these tests.

Attributed source results; no cross-source ranking
Configuration / workloadMetricReported valueEvidence
DeepSeek V4 Flash 0731, long agenticserved rate22.5 · spread 1.04× tokens/smedian of three campaignsOriginal row ↗
Qwen3.8 Flash-Next, long agenticserved rate21.0 · spread 1.01× tokens/smedian of three campaignsOriginal row ↗
GLM5.3 Flash, long agenticserved rate17.7 · spread 1.32× tokens/smedian of three campaignsOriginal row ↗
DeepSeek V4 Flash 0731, long agentic · served rate · conditions
Conditions for this result only
system2 x DGX Spark; 121 GiB unified memory each; 200 GbE
node_count2
parallelismNot specified
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-05
output_tokens512
ignore_eosTrue
prefix_cacheFalse
warmupall workload/user shapes, full generation
repetitions3
concurrency1
measurement_denominatorwhole request wall clock including TTFT
workloadagentic 33k context
modelDeepSeek-V4-Flash-0731
kvfp8_ds_mla
kv_pool_tokens517484
draftDSpark k5 probabilistic

{'spread_ratio': 1.04}

Qwen3.8 Flash-Next, long agentic · served rate · conditions
Conditions for this result only
system2 x DGX Spark; 121 GiB unified memory each; 200 GbE
node_count2
parallelismNot specified
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-05
output_tokens512
ignore_eosTrue
prefix_cacheFalse
warmupall workload/user shapes, full generation
repetitions3
concurrency1
measurement_denominatorwhole request wall clock including TTFT
workloadagentic 33k context
modelQwen3.8-Flash-Next NVFP4
kvBF16
kv_pool_tokens161728
draftNEXTN

{'spread_ratio': 1.01}

GLM5.3 Flash, long agentic · served rate · conditions
Conditions for this result only
system2 x DGX Spark; 121 GiB unified memory each; 200 GbE
node_count2
parallelismNot specified
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-05
output_tokens512
ignore_eosTrue
prefix_cacheFalse
warmupall workload/user shapes, full generation
repetitions3
concurrency1
measurement_denominatorwhole request wall clock including TTFT
workloadagentic 33k context
modelGLM-5.3-Flash
kvBF16
kv_pool_tokens162688
draftDFlash2

{'spread_ratio': 1.32}

AI-assisted source reading and original editorial synthesis; no independent model execution.

Key reading notes

Source identity and full condition receipt

Source identity

Attribution and exact source identity
Original publisherWeZZard
Reference typePerformance report
Model IDQwen3.8-Flash-Next / GLM-5.3-Flash / DeepSeek-V4-Flash-0731
Default recipe artifactNot specified
Runtime topics["vLLM", "SGLang"]
Source revision25e8dbf1b6113dd0b3f12d27a670fb284944d260
Source commit date — not experiment date2026-09-06T00:35:29Z
Source checked2026-10-17
Declared licenseMIT

Reported conditions

Unknown fields remain unspecified; context limits and measured input lengths are different
system2 x DGX Spark; 121 GiB unified memory each; 200 GbE
node_count2
parallelismNot specified
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_dateNot specified
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokensNot specified
output_tokens512
concurrencyNot specified
cache_stateNot specified
quantizationNot specified
speculative_decodingNot specified
temperatureNot specified
top_pNot specified

Fixed-length served rate

Conditions of this profile only
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokensNot specified
output_tokens512
concurrencyNot specified
cache_statefull-shape warmup; per-node JIT cache; see row prefix-cache controls
quantizationNot specified
speculative_decodingmodel-dependent NEXTN / DFlash2 / no-spec arms
temperatureNot specified
top_pNot specified
measurement_denominatorwhole request wall clock including TTFT
Original experiment section

Limits

The complete original tables and raw evidence remain in the linked reports. Selected attributed figures retain their own conditions. Results from different models, weights, runtimes, workloads or cohorts must be read separately.

License scope

Repository metadata only; per-file, model, data and conversion terms remain separate.

This index publishes original explanations, limited attributed factual selections and source pointers. It does not distribute model weights or source repositories. Third-party material retains its own terms.