2cc9370f3d
- vllm.service.j2: branch on role==embedding for --runner pooling --convert embed, --no-enable-prefix-caching, per-model trust_remote_code toggle (needed for nomic-embed-text-v1.5's custom NomicBertModel code), and enforce_eager toggle (needed to avoid CUDA graph capture OOM when co-resident with another vLLM process on this 24GB card). - tasks/verify.yml: split completions vs embedding smoke tests -- embedding-mode instances don't serve /v1/completions. Assert a non-empty embedding vector, not just HTTP 200. - host_vars/astro-orbiter: enable nomic-embed-text-v1.5 (port 8020), lower primary model's gpu_memory_utilization 0.95->0.90 + add enforce_eager after finding 0.95 crash-looped 6-7x before stabilizing with co-resident nomic-embed (real fix, confirmed via NRestarts=0 after clean stop/start, not luck). - Hindsight (values.yaml + externalsecret.yaml): cut LLM + embeddings over to vLLM (:8000, :8020), wire the previously-unset HINDSIGHT_API_EMBEDDINGS_* env vars for the first time, and swap the API key secret source from the Nous fallback item to vllm/api-key (vLLM enforces real auth, llama-swap did not). - README: document the embedding-mode branch, VRAM findings, and a genuine architecture gap -- vLLM's one-model-per-process design cannot replace llama-swap's 5-model LRU roster on this 24GB card, so 21 Hermes profiles' aux-model consumers (Qwen3-8B-no_think, Phi-3.5-mini, Meta-Llama-3.1-8B, Qwen2.5-Coder-14B) and OpenViking's VLM stay on llama-swap. Full teardown (t_6dff1ecc) needs a human decision on the aux-model strategy before it can proceed.
166 lines
9.4 KiB
YAML
166 lines
9.4 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 Superseded the prior AMD RX 5700 / Ollama config below;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, updated t_f5f7e9ad, updated t_441470b9, updated t_c5cef2b2):
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# With models-max=4 and all 6 GGUFs registered, worst case is all 6 loaded simultaneously:
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# Qwen3.8-27B Q4_K_M: ~20.0GB (weights ~17.1GB + KV ~2.9GB @ 65536 ctx, q4_0) ← CORRECTED (ctx rolled back from 128K to 65536, t_c9fed26c 2026-08-18)
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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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# Qwen3-8B Q4_K_M: ~5.5GB (weights ~4.68GB + KV ~0.5GB @ 32K ctx, q4_0)
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# Total worst-case: ~44.5GB >> 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 6 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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# Qwen3.8-27B alone uses ~17,804 MiB (weights+KV @ 65536 ctx); co-residency
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# with Coder (~9GB) = ~27GB > 24GB. LRU eviction handles this automatically.
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# Ryan should be aware this means model-switching always incurs a ~30-60s
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# cold-load latency when switching between Qwen3.8-27B and any other model.
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# Proceeding to models-max=4 as instructed; flagged for Ryan's attention.
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# Router --models-max override for astro-orbiter.
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# UPDATED (t_f5f7e9ad, 2026-08-16): Set to 2 because Qwen3.8-27B-Q4_K_M
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# uses 17,804 MiB at 65536 ctx. Only nomic-embed (558MB, pinned) and ONE
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# generative model can be resident simultaneously. Co-residency of Qwen3.8
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# with any auxiliary model (Phi 8.3GB, Llama 5.9GB, Coder 9GB) exceeds 24GB.
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# models-max=2: slot 1 = nomic-embed (pinned, always loaded), slot 2 = LRU
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# generative model (Qwen3.8 primary, cold-loaded on first request ~30-60s;
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# auxiliary models evict it on demand, and vice versa).
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# NOTE: Qwen3.8 does NOT have load-on-startup — it loads on first request.
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# This avoids an LRU eviction race with nomic-embed at startup.
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# UPDATED (t_72646029, 2026-08-17): CPU offload for Coder + Llama changes the
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# constraint. Coder and Llama now use CPU inference (n-gpu-layers=0). GPU-resident
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# VRAM: Qwen3.8 (~17,804 MiB at 65536 ctx) + nomic-embed (558 MiB, pinned) plus
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# the CUDA-context buffers llama.cpp 6ea215d allocates for the CPU models (~1.4-1.7GB
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# each) = ~20,004 MiB steady-state, below the 24,576 MiB physical limit.
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# CORRECTED (t_c5cef2b2, 2026-08-19): ctx-size was rolled back from 131072 to 65536
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# (t_c9fed26c 2026-08-18). Qwen3.8 VRAM at 65536: 17,804 MiB (not 20,302 MiB).
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# models-max raised to 4: nomic (slot 1, pinned) + Qwen3.8 (slot 2, GPU) +
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# Llama (slot 3, CPU) + Coder (slot 4, CPU). Phi (GPU, ~8.3GB) and new
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# Qwen3-8B (GPU, ~5.5GB) can also be requested but evict Qwen3.8 due to VRAM.
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# models-max=4 is required so CPU-offloaded models count as loaded without
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# evicting Qwen3.8.
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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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# Added t_c5cef2b2 (2026-08-19, War Machine): Qwen3-8B dense 8B model for
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# aux tasks (routing, rewriting, structured extraction, tool-call construction).
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# Source: bartowski/Qwen_Qwen3-8B-GGUF (public, no auth). HF filename is
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# Qwen_Qwen3-8B-Q4_K_M.gguf; stored locally as Qwen3-8B-Q4_K_M.gguf.
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# Exact size verified from HF manifest (content-length): 5,027,784,224 bytes.
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# VRAM: ~4.68GB weights + ~0.5GB KV @ 32K ctx (q4_0) ≈ 5.2GB total.
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# Thinking mode ON by default; use /no_think for latency-sensitive aux tasks.
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- filename: "Qwen3-8B-Q4_K_M.gguf"
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url: "https://huggingface.co/bartowski/Qwen_Qwen3-8B-GGUF/resolve/main/Qwen_Qwen3-8B-Q4_K_M.gguf"
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size_bytes: 5027784224
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source_repo: "bartowski/Qwen_Qwen3-8B-GGUF"
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# --- deploy-vllm role: vllm_models override (t_e6facb19, 2026-08-31) --------
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# Ansible's hash_behaviour is "replace" (see ansible.cfg) — a host_vars list
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# variable REPLACES the role default list wholesale, it does not deep-merge.
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# This is therefore a full copy of roles/deploy-vllm/defaults/main.yml's
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# vllm_models with ONE change: nomic-embed-text-v1.5.enabled flipped to true,
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# now that vllm.service.j2 has an embedding-mode branch (--runner pooling
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# --convert embed --trust-remote-code) tested end-to-end in a shadow window.
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# Primary (Qwen2.5-32B-Instruct-AWQ) and aux (Qwen3-8B-AWQ) entries are
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# unchanged from role defaults — reproduced here only because the whole list
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# must be redefined together. Keep this in sync with defaults/main.yml if the
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# role's model roster changes.
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vllm_models:
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- id: "Qwen2.5-32B-Instruct-AWQ"
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hf_repo: "Qwen/Qwen2.5-32B-Instruct-AWQ"
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role: primary
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quantization: awq
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port: 8000
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max_model_len: 8192
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# 0.95 (role default) OOM'd during CUDA graph capture once nomic-embed
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# (role: embedding, ~814MiB actual, not the nominal 300MB) is co-resident
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# on the same 24GB card (t_e6facb19, 2026-08-31): KV cache allocation
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# succeeded (14,720 tokens) but graph capture needed ~20MiB more than the
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# 0.95 budget left after nomic's share. Two independent, permanent
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# co-residents (unlike t_ca1af9fb's shadow-window test, which had the
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# whole 24GB free) need either a lower utilization ceiling or no graph
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# capture. enforce_eager avoids the whole cudagraph capture memory spike
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# entirely — small throughput cost, no OOM risk, safer for a fixed
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# multi-process VRAM budget than tuning utilization percentages by hand.
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# Even WITH enforce_eager, 0.95 left only ~847MiB genuinely free out of
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# 24576MiB total (23,729MiB used) and both services crash-looped 6-7x
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# during warmup/KV-cache sizing before stabilizing — too fragile for a
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# permanent two-process co-residency. Lowered to 0.90 for real headroom
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# (~1.6GiB free), confirmed clean single-attempt start with no retries.
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gpu_memory_utilization: 0.90
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enforce_eager: true
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enabled: true
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- id: "Qwen3-8B-AWQ"
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hf_repo: "Qwen/Qwen3-8B-AWQ"
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role: aux
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quantization: awq
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port: 8010
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max_model_len: 32768
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gpu_memory_utilization: 0.15
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enabled: false
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- id: "nomic-embed-text-v1.5"
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hf_repo: "nomic-ai/nomic-embed-text-v1.5"
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role: embedding
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quantization: none
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port: 8020
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max_model_len: 2048
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gpu_memory_utilization: 0.05
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trust_remote_code: true
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enabled: true
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