rorappandClaude Opus 5.5
f90b7df31c
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Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
48 lines
1.5 KiB
YAML
48 lines
1.5 KiB
YAML
# Rollen (SPEC-inventory §2/§3). Placement arbeitet sie nach priority ab.
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roles:
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gpu-main:
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priority: 1
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needs: {gpu: true, engine: vllm, min_vram_gb: 16, min_cc: 7.5}
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prefer: largest
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kinds: [llm]
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slots: [tier1-general]
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gpu-flex:
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priority: 2
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needs: {gpu: true, engine: vllm, min_vram_gb: 16, min_cc: 7.5}
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prefer: largest
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kinds: [llm]
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optional: true # ohne zweite große GPU: kein Fallback-Slot, Training nur nachts auf gpu-main
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slots: [tier1-flex]
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gpu-embed:
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priority: 3
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count: 2 # redundant, damit Routing einen GPU-Ausfall übersteht
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needs: {gpu: true, engine: llamacpp, min_vram_gb: 3, min_cc: 5.0}
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prefer: smallest_fitting
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kinds: [embedding, reranker]
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cpu_fallback: true
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slots: [embed-default, rerank-default]
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gpu-small:
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priority: 4
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needs: {gpu: true, engine: llamacpp, min_vram_gb: 4, min_cc: 5.0}
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prefer: smallest_fitting
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kinds: [llm]
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cpu_fallback: true
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slots: [verifier-small, signature-extractor]
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infra:
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priority: 5
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needs: {gpu: false, min_ram_gb: 16}
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slots: []
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tts-hq:
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priority: 6
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share_with: gpu-flex # Qwen3-TTS auf der zweiten Ada, außerhalb der Trainingsfenster
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needs: {gpu: true, engine: pytorch, min_cc: 8.0}
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kinds: [tts]
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optional: true
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slots: [tts-de-hq]
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tts-cpu:
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priority: 7
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share_with: infra # Piper läuft im Infra-LXC mit
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needs: {gpu: false, min_ram_gb: 8}
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kinds: [tts]
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slots: [tts-de]
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