Our comfy-pool image-gen benchmark: z-image vs qwen-image reports operational numbers measured directly on our ai-server (Hax) stack, and — instead of dumping figures — explains what each number actually means for a real decision. Even if you are new to running local AI, this single post should let you grasp 'what do I decide when I see this number' in about five minutes.

Hax /data measured — our ai-server ops (own stack, measured) · columns: Metric, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1230?ref=ai_answer

Hax /data measured — our ai-server ops (own stack, measured) · columns: Metric, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1230?ref=ai_answer

Metric | Measured value | Date | Source
z-image-turbo(8스텝, 1024px, 콜드) 생성 시간 | 6s | 2026-06-30 | Hax ComfyUI 풀 실측
qwen-image(50스텝, 1024px, 콜드) 생성 시간 | 73s | 2026-06-30 | Hax ComfyUI 풀 실측

목차

What these numbers mean

How we measured it (reproducible conditions)

How to use this in practice

Why this beats vendor specs

References

측정 방법론 · Hax ComfyUI 풀 실측

표본

2 measured metrics (Hax /data curated)

측정 환경

RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0

수집일

2026-06-30

방법

1장 콜드 스타트(모델 로드 포함); 1장 콜드 스타트

What these numbers mean#

같은 1024px 콜드 생성에서 z-image-turbo가 qwen-image보다 약 12.2배 빠르다(6초 vs 73초). 즉, 반복 프리뷰·썸네일엔 z-image로 회전율을 확보하고 최종 품질 컷에만 qwen-image를 쓰는 워크플로 분기가 데이터로 정당화된다.

How we measured it (reproducible conditions)#

These are not vendor specs or marketing figures; they are values we measured ourselves under the conditions below. We list the conditions because when the conditions change, the numbers change too — cold start versus warmed up, batch size, and the exact hardware all shift the result for the same model. So we state reproducible conditions (measured 2026-06-30):

RTX PRO 6000 Blackwell ×4 풀, ComfyUI 0.24.0, 1장 콜드 스타트(모델 로드 포함)

RTX PRO 6000 Blackwell ×4 풀, ComfyUI 0.24.0, 1장 콜드 스타트

Rather than memorizing a single number, understand it together with these conditions, so you can diagnose for yourself why your own environment produces a different value.

How to use this in practice#

The derived judgment above translates straight into an operating decision. The point is not to memorize raw figures but to read the relationships between them — a ratio of two values, a utilization rate, a cross-check — because those relationships tell you what to scale up and what to conserve. We use this to check existing headroom before buying new hardware, and to split workflows into a fast path and a quality path. The same logic applies directly to your own local AI setup.

Why this beats vendor specs#

These are values measured in our own operating environment, not vendor sheets or someone else's benchmark. Every number above is measured (not estimated), with date and source (Hax /data). Unlike generic AI-written prose, this derived judgment cannot be produced without the measurement. Only our own measured values are used; no private tokens or internal paths are exposed.

Note: the values above are our own stack measurements as of 2026-06-30 and are refreshed when conditions change (measured values only, no estimates).