Resolved conflicts between local bafd76a (t_c5cef2b2, Qwen3-8B single-variant)
and origin/main 5cf4468 (t_664289a0, Qwen3-8B dual-thinking deployment).
Conflict resolution strategy: took origin/main version throughout — it is the
authoritative result from t_664289a0 which deployed the live no_think config to
astro-orbiter and already reflects the correct production state.
Changes incorporated from origin/main:
- defaults/main.yml: Qwen3-8B-Q4_K_M-no_think model entry (port 8107, n_gpu_layers=99,
chat_template_file), row6 matrix entry, dual-thinking comment block.
- templates/llama-server-router-preset.ini.j2: [Qwen3-8B-Q4_K_M] with sleep-idle-seconds=60
plus new [Qwen3-8B-Q4_K_M-no_think] section with chat-template-file directive.
- templates/qwen3-no-think.jinja.j2: new file — Qwen3 template with enable_thinking=false.
- tasks/models.yml: template deploy task for qwen3-no-think.jinja (chat_templates tag).
- tasks/swapmode.yml: GATE 2 assert updated to 7 models.
- templates/llama-swap-config.yaml.j2: chat_template_file flag support.
Also pulled in monitoring defaults (llm_monitoring_enabled, VRAM exporter settings,
Grafana dashboard vars, Prometheus scrape config) from origin/main monitoring branch.
- Remove global --n-gpu-layers from router unit ExecStart in preset mode
(llama.cpp CLI arg outranked per-model INI n-gpu-layers=0; root cause from
War Machine's run 1). Flag now emitted only in --models-dir mode.
- All 5 preset INI sections carry explicit n-gpu-layers:
Qwen3.8=99, Phi=99, nomic=99, Coder=0, Llama=0.
- host_vars/astro-orbiter: llm_router_models_max 2 -> 4 so CPU-offloaded
models count as loaded without LRU-evicting Qwen3.8.
- defaults: llm_router_coder_gpu_layers / llm_router_llama_gpu_layers = 0.
- verify.yml: fix pre-existing .meta attribute crash in router mode.
- New playbook day2_cpu_offload_aux_models.yml.
Deployed + verified on astro-orbiter (gates A-E PASS): concurrent residency
achieved, Qwen3.8 stays GPU-resident. Measured CPU throughput Llama 9.0 /
Coder 4.7 tok/s. VRAM note: llama.cpp 6ea215d allocates ~1.4-1.7GB CUDA-context
per CPU model even at n-gpu-layers=0 -> ~24,004 MiB steady-state, below the
24,576 MiB physical limit. Comments corrected to match the measurement.
Report: friday/inbox/ryan/2026-08-17-llm-cpu-offload-coder-llama-deployed.md
Ryan-directed model swap (kanban t_f5f7e9ad, 2026-08-16).
Changes:
- Replace [Qwen3.6-35B-A3B-UD-Q4_K_S] with [Qwen3.8-27B-Q4_K_M] in
llama-server-router-preset.ini.j2 (production model slot).
- Qwen3.8-27B: dense 27B VLM, Apache-2.0, Alibaba Aug 2026.
Unsloth Dynamic V3.0 GGUF quantization.
Q4_K_M chosen: 17,106,775,008 bytes, 17.1GB. Measured VRAM: 17,068 MiB
at ctx=32768 (q4_0 KV cache).
- ctx-size set to 32768 (32K) via new variable llm_router_qwen38_ctx_size.
Native context is 262K; 32K chosen to maintain eviction headroom on 24GB RTX 3090.
- models-max reduced 4 -> 2 in host_vars. Qwen3.8 (17.6GB) + nomic-embed
(558MB) exhaust the 24GB card; no auxiliary model can co-reside with Qwen3.8.
LRU eviction handles model switching with ~30-60s cold-load latency.
- llm_router_expected_model_id updated to Qwen3.8-27B-Q4_K_M.
- Qwen3.6 GGUF retained at /opt/models/Qwen3.6-35B-A3B-UD-Q4_K_S.gguf
(not deleted — pending stable period and explicit cleanup task).
- day2_swap_qwen38.yml playbook added for Ansible idempotent redeployment.
Architecture note: Qwen3.8 uses Gated DeltaNet; llama.cpp 6ea215d logs
'fused Gated Delta Net (chunked) not supported, set to disabled'. Inference
works correctly on the non-fused fallback. A llama.cpp update may improve
throughput on the GDN layers.
Smoke test passed: model responded via router endpoint (http://10.1.71.130:8002).
VRAM: 17,630 MiB (Qwen3.8) + 5,928 MiB (Llama-8B concurrent) = 23,558 MiB.
Also commits accumulated but unpushed changes:
- nomic-embed batch-size/rope-scaling fix (t_openviking_embed_batch)
- per-model ctx-size day2 playbook (day2_per_model_ctx_size.yml)
- llama-server-router.service.j2 minor update
- host_vars/astro-orbiter/vars.yml: add Qwen2.5-Coder-14B-Instruct-Q4_K_M.gguf
to llm_staged_models (size_bytes=8988111072, bartowski GGUF public repo).
Updated VRAM note to reflect 4-model roster and LRU eviction semantics.
- defaults/main.yml: add llm_router_coder_ctx_size=16384 and
llm_router_coder_flash_attn=true variables for per-model ctx tuning.
- templates/llama-server-router-preset.ini.j2: add [Qwen2.5-Coder-14B-Instruct-Q4_K_M]
section with alias=Qwen2.5-Coder-14B-Instruct-4bit, ctx-size=16384, flash-attn=true.
- playbooks/day2_add_coder_alias.yml: new playbook that downloads the GGUF (if
absent/mismatched), deploys updated preset INI and systemd unit, restarts
llama-server-router, and verifies all 4 models in /v1/models.
VRAM: Coder ~9GB. Full 4-model co-residency impossible on 24GB — LRU eviction
handles this automatically. Qwen3.6-35B <-> Coder switches incur ~30-60s cold load.
Changes:
- host_vars/astro-orbiter/vars.yml: add llm_router_models_max: 4 (overrides
conservative default of 1). Detailed VRAM OOM risk note included inline:
worst-case 3-model co-residency ~31GB > 24GB RTX 3090. LRU eviction
mitigates in single-user operation; flagged for Ryan's review.
- playbooks/day2_bump_router_models_max.yml: new targeted playbook; deploys
updated router unit, restarts the live service, verifies /health 200 and
/v1/models lists all 3 GGUFs post-restart.
- group_vars/all/semaphore.yml: add llm_router_update_unit template pointing
at the new playbook.
- roles/llm-inference-multimodel/defaults/main.yml: update comment to reflect
the var is now overridden in host_vars rather than 'hardcoded to 1'.
- roles/llm-inference-multimodel/templates/llama-server-router.service.j2:
correct stale 'HARDCODED TO 1' comment — value is variable-driven.
Constraints honored:
- --parallel 1 left untouched (not in scope, not modified anywhere)
- No ad-hoc SSH/systemctl/curl state mutation; all execution via Semaphore
- No installed/vendored code patched
Adds idempotent, data-driven GGUF staging for the two new router models on
astro-orbiter alongside the production Qwen3.6-35B-A3B-UD-Q4_K_S. Both files
were already staged live (byte-exact); this commit codifies them in Ansible so
future re-runs and any new model adds are version-controlled and audit-friendly.
Changes:
- roles/llm-inference-multimodel/tasks/stage_model.yml (NEW)
Idempotent per-model task: stat -> exact byte-size guard -> conditional
get_url -> ownership/mode ensure -> notify router restart handler only on
actual download. Loops from models.yml; nothing hardcoded.
- roles/llm-inference-multimodel/tasks/models.yml
Appends the stage_model.yml loop (tagged: models) after the existing Qwen3.6
download tasks. Data driven from host_vars/astro-orbiter/vars.yml.
- roles/llm-inference-multimodel/defaults/main.yml
Adds llm_staged_models: [] default (empty = safe no-op for hosts with no
staged model list defined).
- roles/llm-inference-multimodel/handlers/main.yml
Adds 'restart llama-server-router on new GGUF' handler. Only fires when
stage_model.yml performs an actual download or corrects ownership/mode.
Normal idempotent re-runs (files already correct) do NOT fire this handler.
- host_vars/astro-orbiter/vars.yml
Adds llm_staged_models list with the two new models:
* Phi-3.5-mini-instruct-Q8_0.gguf (4,061,222,688 bytes,
bartowski/Phi-3.5-mini-instruct-GGUF)
* Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf (4,920,739,232 bytes,
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF)
- playbooks/day1_deploy_llm_inference_multimodel.yml
Updates header comment: removes stale 'Semaphore broken' note, documents
the correct execution channel (Semaphore template
llm_inference_multimodel_stage_models, --tags models).
- group_vars/all/semaphore.yml
Adds llm_inference_multimodel_stage_models template entry (config-as-code).
Template is scoped to --tags models explicitly. Phase 4 (verify) is
EXCLUDED: verify.yml starts llama-server-qwen on :8002, which would collide
with the production llama-server-router.service already running on :8002.
Semaphore template created via API: project 1 / template id 19.
Execution: triggered immediately after this commit via Semaphore REST API.
- Retire llama-server-aux (Phi-4, 8000) and llama-server-toolcall (Mistral-Small-24B, 8001): stopped, disabled, unit files removed from host and Ansible role
- Promote llama-server-qwen (Qwen2.5-14B-Instruct-1M, port 8002) to sole production model, serving both friday and war-machine Hermes profiles
- Verified live: n_ctx=65536/n_ctx_train=1010000, and tool_calls response via /v1/chat/completions probe (no hallucination)
- Deleted superseded GGUF weights (phi-4, mistral-small, orphaned base-Qwen, gemma-2-27b) from astro-orbiter, ~45GB reclaimed
- Updated friday and war-machine Hermes profile configs (model + compression + skills_hub aux) to point at 10.1.71.130:8002
- Ryan explicitly accepted single-model tradeoffs for both profiles
Mistral (llama-server-toolcall, 8001) stopped temporarily on astro-orbiter to
free ~6.2GB VRAM headroom for this test window per Ryan/JARVIS approval.
Not a permanent decommission of Mistral.
- New llama-server-qwen systemd unit template, gated by llm_qwen_service_enabled (default false)
- Idempotent GGUF download task (bartowski Qwen2.5-14B-Instruct-Q5_K_M, stat-guarded)
- Launch flags per local-llm-64k-context-recommendation.md: ctx-size 65536, flash-attn, q8_0 KV cache, batch 2048/ubatch 512, jinja, parallel 1
- verify.yml only starts/verifies the qwen unit when llm_qwen_service_enabled=true
- README: documents live VRAM gate finding (nvidia-smi 2026-08-06: Phi-4+Mistral already ~16.6/24GB, ~7.5GB free -- insufficient for Qwen weights concurrently) and options
- Does NOT touch llama-server-aux (8000) or llama-server-toolcall (8001) service state
