--- # ------------------------------------------------------------------------------ # 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 Superseded the prior AMD RX 5700 / Ollama config below; # drive was transplanted into new hardware, not reinstalled.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, updated t_55c164f5): With models-max=4 and all 4 GGUFs # registered, worst case is all 4 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 @ 32K ctx) # Meta-Llama-3.1-8B Q4_K_M: ~5.6GB (weights ~4.6GB + KV ~0.2GB @ 8K ctx) # Qwen2.5-Coder-14B Q4_K_M: ~9.0GB (weights ~8.4GB + KV ~0.6GB @ 16K ctx) # Total worst-case: ~40.4GB >> 24GB RTX 3090 # # OOM RISK: Full co-residency is impossible on 24GB. LRU eviction prevents this # in practice: models-max=4 means the router can REGISTER 4 models but only keeps # up to 4 LOADED simultaneously — the router will evict the LRU model when a new # one is needed. In single-user homelab operation, only one model is active at a # time. The realistic maximum co-residency is 2 models (whichever was last used). # Qwen3.6-35B alone uses ~21.5GB; co-residency with Coder (~9GB) = ~30.5GB > 24GB. # So effectively: Qwen3.6-35B + any second model will OOM IF both are held concurrently. # LRU eviction handles this automatically — the router evicts the idle model before # loading the new one. Ryan should be aware this means model-switching always incurs # a ~30-60s cold-load latency when switching between Qwen3.6-35B and any other model. # Proceeding to models-max=4 as instructed; 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" - filename: "Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf" url: "https://huggingface.co/bartowski/Qwen2.5-Coder-14B-Instruct-GGUF/resolve/main/Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf" size_bytes: 8988111072 source_repo: "bartowski/Qwen2.5-Coder-14B-Instruct-GGUF"