ad70b3439c
OpenViking Phase 1b (t_34b96e83) — Ryan-approved implementation. Changes: - roles/llm-inference-multimodel/templates/llama-server-router-preset.ini.j2: Add [nomic-embed-text-v1.5] section with embedding=true, n-gpu-layers=99, ctx-size=8192, load-on-startup=true, sleep-idle-seconds=-1. No flash-attn or KV cache params (embedding models use bidirectional forward pass, not autoregressive KV cache). Var: llm_router_nomic_ctx_size. - roles/llm-inference-multimodel/defaults/main.yml: Add llm_router_nomic_ctx_size: 8192. - host_vars/astro-orbiter/vars.yml: Add nomic-embed-text-v1.5-Q4_K_M.gguf to llm_staged_models list (size_bytes: 84106624, source: nomic-ai/nomic-embed-text-v1.5-GGUF). Update VRAM note to reflect 5 registered models (nomic adds ~84MB, negligible given sleep-idle-seconds=-1 / load-on-startup=true pinning). - playbooks/day2_add_nomic_embed.yml: New day2 playbook following the coder-alias pattern: Phase 1: idempotent GGUF download (exact size check) Phase 2: redeploy preset INI Phase 3: redeploy + restart systemd unit Phase 4: /v1/models gate (all 5 models present) Phase 5: /v1/embeddings smoke test (vector returned, not empty) VRAM: ~84MB, always pinned. No impact on generative model LRU behavior. peter-parker Helm values already point at :8002 for the embedding endpoint.
78 lines
4.4 KiB
YAML
78 lines
4.4 KiB
YAML
---
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# ------------------------------------------------------------------------------
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# FILE: ansible/host_vars/astro_orbiter/vars.yml
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# HOST: astro-orbiter (10.1.71.130)
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# ROLE: llama.cpp LLM inference host — Ryzen 7 5800XT / RTX 3090 (ATX rebuild,
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# 2026-08-04). Superseded the prior AMD RX 5700 / Ollama config below;
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# drive was transplanted into new hardware, not reinstalled.
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# ------------------------------------------------------------------------------
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ansible_host: 10.1.71.130
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ansible_user: jarvis
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ansible_ssh_private_key_file: ~/.ssh/id_jarvis
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ansible_become: true
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# LVM root expansion — xlarge template uses sda3 partition, standard VG/LV names
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common_expand_root_lvm: true
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common_root_pv: /dev/sda3
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common_root_vg: ubuntu-vg
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common_root_lv: ubuntu-lv
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# --- Staged GGUF models for the llama.cpp router (:8002) ---------------------
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# Data-driven list consumed by roles/llm-inference-multimodel tasks/models.yml
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# (loop -> tasks/stage_model.yml). Each entry is idempotently staged into
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# /opt/models: stat + EXACT-size check vs HF manifest; skip (no download, no
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# restart) when present + size matches. Source repos are public bartowski GGUFs
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# on HuggingFace (no auth). A router restart is notified ONLY when a new GGUF
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# is actually downloaded.
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# Added 2026-08-12 (War Machine): codify Phi-3.5-mini-instruct-Q8_0 and
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# Meta-Llama-3.1-8B-Instruct-Q4_K_M as router models alongside the production
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# Qwen3.6-35B-A3B-UD-Q4_K_S. The live files were already present/correct on
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# astro-orbiter; this pass codifies them. Future adds = append to this list.
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# Router --models-max override for astro-orbiter.
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# Default in defaults/main.yml is 1 (conservative). Bumped to 4 on 2026-08-12
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# (t_33acbb2e) so the router can keep more than one GGUF resident on-demand
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# and LRU-evict when needed.
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#
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# VRAM NOTE (t_33acbb2e, updated t_55c164f5, updated t_34b96e83): With models-max=4 and all 5 GGUFs
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# registered, worst case is all 5 loaded simultaneously:
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# Qwen3.6-35B-A3B Q4_K_S: ~21.5GB (weights ~19.5GB + KV ~2GB @ 64K ctx, q4_0)
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# Phi-3.5-mini-instruct Q8_0: ~4.3GB (weights ~3.8GB + KV ~0.5GB @ 32K ctx)
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# Meta-Llama-3.1-8B Q4_K_M: ~5.6GB (weights ~4.6GB + KV ~0.2GB @ 8K ctx)
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# Qwen2.5-Coder-14B Q4_K_M: ~9.0GB (weights ~8.4GB + KV ~0.6GB @ 16K ctx)
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# nomic-embed-text-v1.5 Q4_K_M: ~0.09GB (~84MB, embedding only — no KV cache)
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# Total worst-case: ~40.5GB >> 24GB RTX 3090
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#
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# OOM RISK: Full co-residency is impossible on 24GB. LRU eviction prevents this
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# in practice: models-max=4 means the router can REGISTER 5 models but only keeps
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# up to 4 LOADED simultaneously — the router will evict the LRU model when a new
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# one is needed. nomic-embed-text-v1.5 is pinned via sleep-idle-seconds=-1 and
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# load-on-startup=true but it uses only ~84MB, so it never meaningfully changes
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# the budget. In single-user homelab operation, only one generative model is active
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# at a time alongside the always-resident embedding model.
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# Qwen3.6-35B alone uses ~21.5GB; co-residency with Coder (~9GB) = ~30.5GB > 24GB.
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# LRU eviction handles this automatically — the router evicts the idle model before
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# loading the new one. Ryan should be aware this means model-switching always incurs
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# a ~30-60s cold-load latency when switching between Qwen3.6-35B and any other model.
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# Proceeding to models-max=4 as instructed; flagged for Ryan's attention.
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llm_router_models_max: 4
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llm_staged_models:
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- filename: "Phi-3.5-mini-instruct-Q8_0.gguf"
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url: "https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF/resolve/main/Phi-3.5-mini-instruct-Q8_0.gguf"
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size_bytes: 4061222688
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source_repo: "bartowski/Phi-3.5-mini-instruct-GGUF"
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- filename: "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"
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url: "https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"
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size_bytes: 4920739232
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source_repo: "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF"
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- filename: "Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf"
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url: "https://huggingface.co/bartowski/Qwen2.5-Coder-14B-Instruct-GGUF/resolve/main/Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf"
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size_bytes: 8988111072
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source_repo: "bartowski/Qwen2.5-Coder-14B-Instruct-GGUF"
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- filename: "nomic-embed-text-v1.5-Q4_K_M.gguf"
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url: "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/resolve/main/nomic-embed-text-v1.5.Q4_K_M.gguf"
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size_bytes: 84106624
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source_repo: "nomic-ai/nomic-embed-text-v1.5-GGUF"
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