40 lines
1.5 KiB
Django/Jinja
40 lines
1.5 KiB
Django/Jinja
[Unit]
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Description=llama-server (shadow) — Qwen2.5-14B-Instruct Q5_K_M (OpenAI-compatible inference, 64K ctx)
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After=network.target nvidia-persistenced.service
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Wants=nvidia-persistenced.service
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[Service]
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Type=simple
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User={{ llm_service_user }}
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Group={{ llm_service_user }}
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Environment="HOME=/home/{{ llm_service_user }}"
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ExecStart={{ llm_binary_path }} \
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--model {{ llm_qwen_model_path }} \
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--host 0.0.0.0 \
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--port {{ llm_qwen_port }} \
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--n-gpu-layers {{ llm_qwen_gpu_layers }} \
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--ctx-size {{ llm_qwen_ctx_size }} \
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--flash-attn on \
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--cache-type-k q8_0 --cache-type-v q8_0 \
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--batch-size {{ llm_qwen_batch_size }} --ubatch-size {{ llm_qwen_ubatch_size }} \
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--jinja \
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--parallel {{ llm_qwen_parallel }} \
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--metrics
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# Shadow-deployment candidate per local-llm-64k-context-recommendation.md.
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# NOT yet cleared for production Hermes profile routing — must pass
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# scripts/tool-calling-validation.sh AND have verified n_ctx >= 64000 from
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# /v1/models before any repoint decision.
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# VRAM GATE: as of 2026-08-06, Phi-4(8000)+Mistral(8001) already consume
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# ~16.6GB/24GB (7.5GB free). This model's weights alone are ~10-12GB — does
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# NOT fit concurrently without freeing VRAM. Do not enable this unit until
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# that is resolved (see role README "Qwen shadow deployment — VRAM gate").
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Restart=on-failure
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RestartSec=10
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TimeoutStartSec=600
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StandardOutput=journal
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StandardError=journal
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SyslogIdentifier=llama-server-qwen
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[Install]
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WantedBy=multi-user.target
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