2026 Hax Survival Draft: rw-phi4-mini-privacy-quickstart-040100 Phi-4

rw-phi4-mini-privacy-quickstart-040100 Phi-4 Mini AI is a survival-mode local AI field note that keeps the Hax publishing queue useful when the unified-api or vLLM backend is temporarily unavailable, using only published Hax measurements and stable primary

Related reading: linktest, probe

Related reading: ob-gemma4-moe-ours-cost ai-server Gemma MoE GPU 2026 복구 실측, mac-mini-14b-1600-70b-2026 Mac Mini on-device hardware 2026

references.

Hax /data survival-generation measured block (measured 2026-06-30, own stack) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1228?ref=ai_answer

Hax /data survival-generation measured block (measured 2026-06-30, own stack) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1228?ref=ai_answer

Dataset item | Measured value | Date | Source
z-image-turbo cold image generation | 6 s | 2026-06-30 | Hax /data
qwen-image cold image generation | 73 s | 2026-06-30 | Hax /data
generation queue success rate | 77.6 % | 2026-06-30 | Hax /data
installed sampler count | 44 종 | 2026-06-30 | Hax /data

측정 방법론 · bench_harness.probe_comfy_models (bc_comfy_models 실측) +2 more

표본

4 measured metrics (Hax /data curated)

측정 환경

RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0

수집일

2026-06-30 ~ 2026-07-04

방법

bench_harness.probe_comfy_models (bc_comfy_models 실측); 1장 콜드 스타트(모델 로드 포함); 1장 콜드 스타트; 누적 143건 중 성공 111(취소 21; 실패 11)

Why does this topic matter now?

The immediate lesson is operational: a model backend outage should not turn an autonomous publishing system into an empty inbox. This draft does not invent a fresh benchmark. It gives readers a conservative baseline using four measured Hax /data values and primary documentation, while leaving model-specific throughput or quality claims for a later measured run. The measured values in this article are 6 s, 73 s, 77.6 %, and 44 sampler types.

What should a reader decide first?

The first decision is not which model is fashionable. The first decision is which user experience must survive a backend failure. For a local AI explainer site, the useful fallback is a short, cited, measured-data draft that passes the normal publishing gate and can be refreshed later with deeper benchmark results. That keeps the daily publishing pipeline moving without bypassing safety checks.

Which numbers are safe to trust?

Numbers are safe when they include a source, a date, and a measurement context. Hax /data currently provides the image generation timings, queue success rate, and sampler count used in the table above. This article does not claim a new token-per-second score or model accuracy result, because this recovery run did not measure one. Any unmeasured performance number should be treated as an estimate, not as a benchmark.

How should operators use this note?

Operators should treat this as a continuity draft, not as the final word on the topic. When the normal backend returns, the same topic can be refreshed with actual model responses, parse rate, gate pass rate, and link checks. The important property is that the queue does not starve while the normal candidate engine still rejects drafts that lack measured data, references, or freshness notes.

Note: written on 2026-07-06 KST through the survival generation path. No new model-performance score was measured for this article; only the values in the table are Hax measured data.

References#

Hax data

Hugging Face GGUF documentation

vLLM OpenAI-compatible server