---
# ------------------------------------------------------------------------------
# FILE: ansible/host_vars/astro_orbiter/vars.yml
# HOST: astro-orbiter (10.1.71.130)
# ROLE: llama.cpp LLM inference host — Ryzen 7 5800XT / RTX 3090 (ATX rebuild,
#       2026-08-04). Superseded the prior AMD RX 5700 / Ollama config below;
#       drive was transplanted into new hardware, not reinstalled.
# ------------------------------------------------------------------------------

ansible_host: 10.1.71.130
ansible_user: jarvis
ansible_ssh_private_key_file: ~/.ssh/id_jarvis
ansible_become: true

# LVM root expansion — xlarge template uses sda3 partition, standard VG/LV names
common_expand_root_lvm: true
common_root_pv: /dev/sda3
common_root_vg: ubuntu-vg
common_root_lv: ubuntu-lv

# --- Staged GGUF models for the llama.cpp router (:8002) ---------------------
# Data-driven list consumed by roles/llm-inference-multimodel tasks/models.yml
# (loop -> tasks/stage_model.yml). Each entry is idempotently staged into
# /opt/models: stat + EXACT-size check vs HF manifest; skip (no download, no
# restart) when present + size matches. Source repos are public bartowski GGUFs
# on HuggingFace (no auth). A router restart is notified ONLY when a new GGUF
# is actually downloaded.
# Added 2026-08-12 (War Machine): codify Phi-3.5-mini-instruct-Q8_0 and
# Meta-Llama-3.1-8B-Instruct-Q4_K_M as router models alongside the production
# Qwen3.6-35B-A3B-UD-Q4_K_S. The live files were already present/correct on
# astro-orbiter; this pass codifies them. Future adds = append to this list.
# Router --models-max override for astro-orbiter.
# Default in defaults/main.yml is 1 (conservative). Bumped to 4 on 2026-08-12
# (t_33acbb2e) so the router can keep more than one GGUF resident on-demand
# and LRU-evict when needed.
#
# VRAM NOTE (t_33acbb2e): With models-max=4 and all 3 current GGUFs plus
# headroom for one more, worst case is all 3 loaded simultaneously:
#   Qwen3.6-35B-A3B Q4_K_S:       ~21.5GB (weights ~19.5GB + KV ~2GB @ 64K ctx, q4_0)
#   Phi-3.5-mini-instruct Q8_0:   ~4.3GB  (weights ~3.8GB + KV ~0.5GB @ 64K ctx)
#   Meta-Llama-3.1-8B Q4_K_M:    ~5.6GB  (weights ~4.6GB + KV ~1.0GB @ 64K ctx)
#   Total worst-case:             ~31.4GB  >  24GB RTX 3090
#
# OOM RISK: If all 3 models are loaded concurrently the card will OOM. The
# router's LRU eviction means this only occurs if all 3 models receive a
# concurrent request before any model completes its response (very unlikely
# in single-user homelab operation). However, Ryan should be aware. A safe
# alternative would be models-max=2 (allows Qwen + one small model resident
# simultaneously: ~21.5 + 5.6 = ~27.1GB still tight). Proceeding to 4 as
# instructed per task t_33acbb2e; flagged for Ryan's attention.
llm_router_models_max: 4

llm_staged_models:
  - filename: "Phi-3.5-mini-instruct-Q8_0.gguf"
    url: "https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF/resolve/main/Phi-3.5-mini-instruct-Q8_0.gguf"
    size_bytes: 4061222688
    source_repo: "bartowski/Phi-3.5-mini-instruct-GGUF"
  - filename: "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"
    url: "https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"
    size_bytes: 4920739232
    source_repo: "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF"