chore(config): fix ignored path .qwen/temp to .qwen/tmp

Assess whether the Colibrì disk-streaming MoE inference engine fits the AMD ROCm single-GPU setup or fills a gap beyond the existing llama.cpp/OmniRoute/Qdrant/Neo4j/ComfyUI stack. Conclusion: worth a passing watch, not worth integrating today.

.env.example

@@ -105,34 +105,6 @@ COMFYUI_PGID=
 HOST_VIDEO_GID=
 HOST_RENDER_GID=
 
-# --- llama.cpp / fast model (second, always-resident instance — see
-# docs/research/fast-model-choice.md and issue #44) ---
-# Qwen3-4B-Instruct-2507: architecturally non-thinking (never emits
-# <think> blocks, unlike Qwen3-1.7B/0.6B which need a per-call toggle) —
-# picked specifically so it stays fast enough for qwen-code's Auto Mode
-# classifier (Stage 1 wants ~300ms). Same publisher (unsloth) as the main
-# model for consistency.
-LLAMA_FAST_MODEL_FILE=Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf
-# Same reasoning as LLAMA_GPU_LAYERS above — full GPU offload, this model
-# is dense too.
-LLAMA_FAST_GPU_LAYERS=999
-# --ctx-size is the TOTAL across every LLAMA_FAST_PARALLEL slot, not per
-# request — same halving already called out for the main model above.
-# Was PARALLEL=2, silently halving this to 4096/slot — too small: a real
-# classifier call (hints + environment + recent tool-call history) hit
-# "exceeds the available context size (4096 tokens)" in practice, which
-# qwen-code surfaces as "Auto Mode couldn't classify this action
-# (Classifier stage 1 unavailable)" — see issue #5. Fixed by dropping to
-# a single slot instead of raising ctx-size (no extra VRAM, and this
-# service doesn't need concurrent classifier calls the way the main
-# model needs concurrent chat sessions) — the full 8192 now goes to the
-# one slot. If hints.allow/softDeny/hardDeny ever approach their
-# 50-entries-each ceiling, raise LLAMA_FAST_CTX_SIZE instead — qwen-code
-# caps those at 200 chars x 150 entries plus 40,000 chars of
-# historical-action context, which can exceed 8192 tokens worst-case.
-LLAMA_FAST_CTX_SIZE=8192
-LLAMA_FAST_PARALLEL=1
-
 # --- ComfyUI diffusion model (Qwen-Image, FP8 — see docs/research/
 # image-generation-model-choice.md and issue #42) ---
 # Three files: diffusion weights, text encoder, VAE — all from the official

.gitignore

@@ -5,4 +5,4 @@
 data/
 .leankg/
 .cache/
-.qwen/temp
+.qwen/tmp

docker-compose.yml

@@ -15,8 +15,7 @@ services:
     ipc: host
     # Caps this process's HIP hardware-queue allocation — works around
     # ROCm/ROCm#5706 (GPU pinned at 100%/boost-clock whenever two
-    # concurrent HIP contexts touch this card, confirmed on real hardware
+    # concurrent HIP contexts touch this card). See the research doc above.
-    # against llama-server-fast below). See the research doc above.
     environment:
       - GPU_MAX_HW_QUEUES=1
     volumes:
@@ -47,49 +46,6 @@ services:
       - "lazytainer.group.llamaserver.inactiveTimeout=${LAZYTAINER_INACTIVE_TIMEOUT:-900}"
       - "lazytainer.group.llamaserver.minPacketThreshold=2"
 
-  llama-server-fast:
-    image: ghcr.io/ggml-org/llama.cpp:server-rocm
-    container_name: llama-server-fast
-    devices:
-      - /dev/kfd
-      - /dev/dri
-    group_add:
-      - "${HOST_VIDEO_GID:?run scripts/update.sh first to resolve this}"
-      - "${HOST_RENDER_GID:?run scripts/update.sh first to resolve this}"
-    security_opt:
-      - seccomp=unconfined
-    ipc: host
-    # See llama-server's identical setting above — same fix, same bug.
-    environment:
-      - GPU_MAX_HW_QUEUES=1
-    volumes:
-      - models:/models
-    command: >
-      -m /models/${LLAMA_FAST_MODEL_FILE:-Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf}
-      --host 0.0.0.0
-      --port 8080
-      --n-gpu-layers ${LLAMA_FAST_GPU_LAYERS:-999}
-      --ctx-size ${LLAMA_FAST_CTX_SIZE:-8192}
-      --parallel ${LLAMA_FAST_PARALLEL:-1}
-      --flash-attn on
-      --cache-type-k q8_0
-      --cache-type-v q8_0
-      --reasoning off
-      --jinja
-    # Second, always-resident llama.cpp instance — small non-thinking model
-    # used as qwen-code's Auto Mode classifier fastModel, alongside the main
-    # 27B instance above. See docs/research/fast-model-choice.md and #44.
-    # Same ai-stack-only pattern as llama-server: no published host port.
-    expose:
-      - "8080"
-    restart: unless-stopped
-    networks: [ai-stack]
-    labels:
-      - "lazytainer.group.llamaserverfast.sleepMethod=stop"
-      - "lazytainer.group.llamaserverfast.ports=8080"
-      - "lazytainer.group.llamaserverfast.inactiveTimeout=${LAZYTAINER_INACTIVE_TIMEOUT:-900}"
-      - "lazytainer.group.llamaserverfast.minPacketThreshold=2"
-
   # ponytail: one-off downloader, not a standing service — run via
   # `docker compose --profile tools run --rm downloader`. Folded into
   # scripts/update.sh, which runs this every time; the `test -f` guard is
@@ -111,26 +67,9 @@ services:
         curl -L --fail --create-dirs -o /models/${LLAMA_MODEL_FILE:-Qwen3.8-27B-UD-Q4_K_XL.gguf}
         https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/resolve/main/${LLAMA_MODEL_FILE:-Qwen3.8-27B-UD-Q4_K_XL.gguf}
 
-  # Same test -f guard pattern as downloader above — fetches the second,
-  # smaller model for llama-server-fast. See issue #44.
-  downloader-fast:
-    image: curlimages/curl:latest
-    profiles: ["tools"]
-    user: root
-    volumes:
-      - models:/models
-    entrypoint: ["sh", "-c"]
-    command:
-      - >
-        test -f /models/${LLAMA_FAST_MODEL_FILE:-Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf} &&
-        echo "already downloaded, skipping" ||
-        curl -L --fail --create-dirs -o /models/${LLAMA_FAST_MODEL_FILE:-Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf}
-        https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF/resolve/main/${LLAMA_FAST_MODEL_FILE:-Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf}
-
   # Fetches the three Qwen-Image FP8 files ComfyUI needs (diffusion model,
-  # text encoder, VAE) — same test -f guard pattern as downloader/
+  # text encoder, VAE) — same test -f guard pattern as downloader above.
-  # downloader-fast above. See docs/research/image-generation-model-choice.md
+  # See docs/research/image-generation-model-choice.md and issue #42.
-  # and issue #42.
   #
   # ponytail: target paths assume ComfyUI's standard models/ layout under
   # BASE_STORAGE_PATH (/storage) — same "not independently confirmed against
@@ -184,7 +123,7 @@ services:
       # This image also wants GID env vars directly (its own README asks
       # for both these and group_add above) — same HOST_VIDEO_GID/
       # HOST_RENDER_GID resolved by scripts/update.sh, shared with
-      # llama-server/llama-server-fast now instead of comfyui-only vars.
+      # llama-server now instead of comfyui-only vars.
       - PUID=${COMFYUI_PUID}
       - PGID=${COMFYUI_PGID}
       - VIDEO_GID=${HOST_VIDEO_GID}
@@ -220,8 +159,6 @@ services:
     depends_on:
       llama-server:
         condition: service_started
-      llama-server-fast:
-        condition: service_started
     volumes:
       - omniroute-data:/app/data
     env_file: .env

docs/research/colibri-ai-stack-evaluation.md

@@ -0,0 +1,222 @@
+# Evaluating Colibrì (JustVugg/colibri) for this stack
+
+**Date:** 2026-09-08
+**Scope:** The user flagged https://github.com/JustVugg/colibri as something that "could revolutionize"
+this self-hosted AI stack. What is Colibrì actually, is it compatible with this stack's AMD
+ROCm/HIP-only single-GPU setup, and — even if compatible — does it fill a real gap versus what
+llama.cpp, OmniRoute, Qdrant, Neo4j, and ComfyUI already do here?
+
+## 1. What Colibrì actually is
+
+Colibrì is a pure-C, zero-runtime-dependency inference engine whose specific trick is treating
+"storage, RAM, and VRAM as a single inference hierarchy" so that huge mixture-of-experts (MoE)
+models — far bigger than any one machine's VRAM+RAM — can still run, by keeping the small dense
+layers resident and streaming the (much larger) set of routed experts from disk on demand with an
+LRU/"hot-store" cache and router-lookahead prefetching:
+
+> "Colibrì is an open-source inference engine designed to run frontier mixture-of-experts (MoE)
+> models on consumer hardware... The fundamental approach uses 'a JIT, but for weights' — parameters
+> are staged across storage tiers (VRAM/RAM/NVMe) based on measured routing patterns rather than kept
+> resident."
+— https://raw.githubusercontent.com/JustVugg/colibri/main/README.md
+
+It ships single-C-file implementations for eight specific model families — GLM-5.2/5.3,
+GLM-5.3-Flash, Inkling, Kimi K3, DeepSeek V4 Flash, Qwen3.8-Flash-Next, Qwen3.6 (35B-A3B), and OLMoE
+— each requiring model weights pre-converted into Colibrì's own container format (`coli convert`),
+not arbitrary GGUF files:
+
+> "Eight model families with single C file implementations... GLM-5.2/5.3 | 744B | 372GB | 16GB+ ...
+> Kimi K3 | 2.8T | 1.6TB | 32GB+"
+— https://raw.githubusercontent.com/JustVugg/colibri/main/README.md
+
+It's meant to be run either from prebuilt binaries/releases, built from source (`./setup.sh` under
+`c/`), or via Docker (`docker/Dockerfile`, `docker/Dockerfile.slim`, `docker/docker-compose.yml` exist
+in-repo — confirmed present via the GitHub contents API, https://api.github.com/repos/JustVugg/colibri/contents/docker),
+exposing an OpenAI- and Anthropic-compatible HTTP API (`coli serve`, default `http://127.0.0.1:8000/v1`,
+plus `/v1/messages`) — the same shape OmniRoute already expects from a provider, per third-party
+summaries of `docs/api.md` and `docs/serve_protocol.md`
+([search result summary, secondary](https://github.com/JustVugg/colibri/blob/main/docs/api.md)).
+
+It launched July 10, 2026 and went viral on Hacker News the same day (453 points) on the strength of
+running the 744B-parameter GLM-5.2 model on a 25GB-RAM consumer box:
+
+> "A new inference engine called 'Colibrì' has emerged that can run the massive AI 'GLM-5.2,' with 744
+> billion parameters, on a regular PC with 25GB of memory."
+— https://gigazine.net/gsc_news/en/20260710-colibri-glm/ (secondary coverage)
+
+## 2. Hardware/runtime requirements — AMD ROCm or NVIDIA-only?
+
+**This is the load-bearing question given this stack runs llama.cpp on ROCm/HIP, not CUDA, on a
+single AMD Radeon AI PRO R9700.** The answer is more nuanced than a flat yes/no — verified against
+source, not just README prose:
+
+- **The engine is CPU-first; a GPU is optional at all.** `docs/quickstart.md` states plainly: "You do
+  **not** need a GPU. A GPU only helps if you have one; the engine runs CPU-only by default."
+  — https://raw.githubusercontent.com/JustVugg/colibri/main/docs/quickstart.md
+- **AMD/ROCm support is real, shipped, and reasonably recent — not just a README claim.** It was
+  requested in [issue #69](https://github.com/JustVugg/colibri/issues/69) (opened 2026-07-11, "No
+  ROCM support"), the maintainer confirmed it was mechanically straightforward since HIP closely
+  mirrors CUDA, an initial PR (#112) added it but was **closed unmerged**, and a follow-up PR — tracked
+  as [#339](https://github.com/JustVugg/colibri/issues/69) — landed the actual mechanism that shipped:
+  a single shared CUDA kernel source (`backend_cuda.cu`) compiled either by `nvcc` or by `hipcc`
+  against a compatibility header:
+
+  > "backend_gpu_compat.h — 'one GPU backend source, two vendors.' ... maps CUDA runtime calls to HIP
+  > equivalents when compiled by hipcc with `HIP=1`... handles architecture-specific guards for rocWMMA
+  > availability and matrix core support across different GPU architectures (gfx906, gfx908, gfx11xx,
+  > etc.)."
+  — https://raw.githubusercontent.com/JustVugg/colibri/main/c/backend_gpu_compat.h (confirmed present
+  in the current `main` branch — this is not a stale/unmerged branch)
+
+  This shipped in a **tagged release**, not just an open PR — `CHANGELOG.md` lists "AMD GPU support" as
+  part of v1.1.0 (2026-07-22): "AMD GPU support, dual-SSD streaming, fmt=5/fmt=6 quantization formats."
+  — https://raw.githubusercontent.com/JustVugg/colibri/main/CHANGELOG.md
+- **Notably, the community contributor who tested it used an RX 9070 XT / gfx1201** — the same RDNA4
+  architecture generation as this stack's Radeon AI PRO R9700 — per the PR #339 description: "validated
+  across CPU builds, HIP testing on gfx1201, and NVIDIA compatibility verification." That's a genuinely
+  favorable, non-generic signal for this specific card's GPU family, better than "AMD support exists
+  somewhere."
+- **But ROCm support is thinner and less documented than the CUDA/Metal paths.** `docs/` has `cuda.md`,
+  `metal.md`, `metal_implementation.md`, and `vulkan.md`, but **no `rocm.md` or `hip.md`** (confirmed via
+  the GitHub contents API listing of `docs/`, https://api.github.com/repos/JustVugg/colibri/contents/docs).
+  Model-specific tuning docs are written CUDA-only with no AMD mention at all — e.g.
+  `docs/qwen36-cuda-tier.md` references `COLI_CUDA=1`, `backend_cuda.cu`, and lists test hardware as
+  "RTX 3070 8 GB + Quadro RTX 4000" (both NVIDIA), with zero AMD/ROCm/HIP text anywhere in that
+  document. — https://raw.githubusercontent.com/JustVugg/colibri/main/docs/qwen36-cuda-tier.md
+  So: the *general* GPU-acceleration mechanism supports ROCm/HIP and has been community-validated on
+  hardware close to the R9700, but the *per-model* tiering/tuning documentation and (presumably) most
+  of the maintainer's own benchmarking is CUDA-first. Treat AMD support as functional-but-secondary,
+  not a first-class, symmetrically-tested backend.
+- **A separate GPU-agnostic path also exists**: a Vulkan backend (`backend_vulkan.c`, confirmed present
+  in the `c/` directory listing) that the project positions as covering "AMD via Mesa/RADV" as a
+  vendor-neutral fallback, independent of the HIP path above — this would also be viable on the R9700
+  in principle, though vendor-neutral compute back ends are typically slower than a vendor SDK path
+  (HIP/ROCm) and no R9700/gfx1201-specific Vulkan numbers were found in the docs reviewed.
+
+**Bottom line on hardware fit: not a blocker.** Unlike a hard CUDA-only dependency, Colibrì's AMD/HIP
+path is real, shipped in a release, and specifically exercised on the same RDNA4 family as this
+stack's GPU — this is not a disqualifying finding the way it would be for a CUDA-only tool.
+
+## 3. License
+
+Apache License 2.0, confirmed by fetching `LICENSE` directly from the repo — a standard permissive
+license, fine for self-hosted use here (commercial or non-commercial, modification, redistribution all
+permitted, patent grant included, "AS IS" with no warranty).
+— https://raw.githubusercontent.com/JustVugg/colibri/main/LICENSE
+
+Model weights are licensed separately from the engine — e.g. the README notes "GLM-5.2 weights
+released by Z.ai under MIT"
+(https://raw.githubusercontent.com/JustVugg/colibri/main/README.md) — so each model's own license
+would need checking before use, same as with any GGUF today.
+
+## 4. Maturity signals
+
+Pulled from the GitHub API (https://api.github.com/repos/JustVugg/colibri) and the changelog:
+
+| Signal | Value |
+|---|---|
+| Repo created | 2026-07-01 |
+| First tagged release (v1.0.0) | 2026-07-19 |
+| Current version (as of today) | 1.10.2 (2026-09-06) |
+| Age at time of writing | ~10 weeks |
+| Stars / Forks | 27,047 / 2,963 |
+| Open issues | 104 |
+| Top contributor | JustVugg — 1,077 commits |
+| #2 contributor | ZacharyZcR — 163 commits |
+| Total contributors | 100+ (long tail, most in single digits) |
+| License | Apache 2.0 |
+| Archived? | No |
+
+Read honestly, this is **a viral, very-early-stage, single-maintainer-dominated project**, not a
+mature or slow-burn one:
+
+- It is ~10 weeks old today. The star count (27k) is wildly disproportionate to that age and reflects
+  a Hacker News front-page moment (453 points the day it launched,
+  https://gigazine.net/gsc_news/en/20260710-colibri-glm/), not organic multi-year adoption. Stars are
+  a popularity signal, not a quality proof, and here the ratio (huge stars, ~10 weeks old, one dominant
+  committer) is itself a maturity red flag worth naming rather than a mark in its favor.
+- Commit/release activity is genuinely fast — 14 tagged releases in under 8 weeks (v1.0.0 on 2026-07-19
+  through v1.10.2 on 2026-09-06, https://raw.githubusercontent.com/JustVugg/colibri/main/CHANGELOG.md)
+  — so it is actively maintained day-to-day, not abandoned. But that pace also means breaking changes
+  and security patches are frequent: v1.6.2 (2026-08-14) was itself "a security release: six
+  privately-reported memory-safety issues fixed... from untrusted input," and v1.10.2 (2026-09-06)
+  again touched "security fixes for image API" — both signs of a codebase still finding its footing on
+  hardening, not evidence of instability being the norm, but worth weighing given this stack would be
+  exposing any such server on an internal network via OmniRoute.
+- Contribution concentration is heavy: the maintainer (JustVugg) has ~6.6x the commits of the next
+  contributor, and the rest of the 100+ contributor list trails off into single-digit-commit
+  drive-by PRs (per the GitHub contributors API,
+  https://api.github.com/repos/JustVugg/colibri/contributors) — a classic "one person's viral project
+  plus a wave of small first-time PRs" shape, not an established multi-maintainer team.
+- The AMD/ROCm feature specifically has a short, thin history: requested 2026-07-11, shipped 2026-07-22
+  (11 days later, in v1.1.0), documented only implicitly (no dedicated `docs/hip.md`/`rocm.md`, unlike
+  every other backend) — i.e., it is the newest and least-independently-verified of the project's four
+  GPU backends (CUDA, Metal, Vulkan, HIP).
+
+## 5. What capability gap it would actually fill in this stack
+
+Concretely comparing against what's already running (`docker-compose.yml`):
+
+- **llama.cpp (ROCm) already fully GPU-resides the current model** — `--n-gpu-layers 999` on the
+  llama-server service means the whole Qwen3.8-27B-class GGUF sits in the R9700's VRAM and runs at
+  normal, fast, interactive token rates. Colibrì's entire value proposition is the *opposite* case:
+  models **too large to fit in VRAM+RAM at all**, accepted at the cost of streaming most of the model
+  from disk on every forward pass. For a model that already fits on this GPU (which is the whole point
+  of the current setup), Colibrì offers no benefit — llama.cpp is already doing the fast thing.
+- **The actual gap it could fill is running models this stack categorically cannot run today** — e.g.
+  GLM-5.2 (744B), DeepSeek V4 Flash (284B), or Kimi K3 (2.8T), none of which would ever fit on a single
+  R9700 regardless of quantization. Colibrì's own benchmarks make the cost of that explicit and, unlike
+  its capability claims, these are the project's self-reported numbers, not independently reproduced —
+  flagged as such:
+
+  > "6× RTX 5090 (full residency): 5.8-6.8 tokens/second decode... 128GB CPU-only desktop: ~1.8
+  > tokens/second (warm)... Single RTX 5070 Ti: 1.07 tokens/second... 25GB dev box: 0.05-0.1
+  > tokens/second (baseline)."
+  — https://raw.githubusercontent.com/JustVugg/colibri/main/README.md (self-reported, unverified by
+  any third party found during this research)
+
+  At 0.05–2 tokens/second on any hardware remotely resembling a single-GPU workstation, this is not
+  usable for the interactive coding-CLI workloads this stack is built around (Claude Code CLI, Kimi
+  CLI, etc. routed through OmniRoute per the README) — those need low per-token latency for tool-calling
+  round trips, not throughput measured in seconds per token. It would only be plausible as an
+  occasional, patient, offline/batch capability (e.g. "let a huge model chew on a large doc overnight")
+  layered in *alongside*, not instead of, the current llama.cpp path.
+- **Disk footprint is a real new cost, not a marginal one**: 167GB–1.6TB per model
+  (https://raw.githubusercontent.com/JustVugg/colibri/main/README.md), which would need to be
+  provisioned in addition to the existing `models` Docker volume, GGUF downloads, Qdrant/Neo4j
+  volumes, and ComfyUI's model files already on this box.
+- **No overlap or replacement value for OmniRoute, Qdrant, Neo4j, or ComfyUI** — Colibrì is strictly an
+  inference-engine alternative to llama.cpp for a narrow, specific list of very large MoE models; it
+  does nothing related to gateway/key-management (OmniRoute's job), vector/graph storage (Qdrant/Neo4j's
+  job), or image generation (ComfyUI's job). Its own `coli serve` OpenAI/Anthropic-compatible endpoint
+  could in principle be registered as another OmniRoute provider the same way llama-server is today —
+  that part is mechanically plausible — but it would be adding a second, much slower inference backend
+  next to the existing fast one, not replacing or upgrading anything currently in the stack.
+
+## 6. Bottom line
+
+**Not a fit for this stack right now, and the "revolutionize" framing does not hold up** — but for a
+more specific reason than "wrong GPU vendor":
+
+- **ROCm/AMD support is real and not the blocker one might expect.** It shipped in a tagged release
+  (v1.1.0, 2026-07-22), lives in a compatibility header confirmed present on `main`
+  (`c/backend_gpu_compat.h`), and was community-tested on an RX 9070 XT / gfx1201 — the same RDNA4
+  family as this stack's R9700. This is genuinely worth noting as a positive, since it's the kind of
+  thing that usually *is* disqualifying for AMD-only stacks and here it isn't.
+- **The disqualifying issue is fit, not hardware**: Colibrì solves "run a model way too big for your
+  VRAM+RAM by streaming most of it from disk," at 0.05–2 tokens/second. This stack's actual situation is
+  the opposite — a model sized to fully fit and run fast on a single GPU via llama.cpp. Colibrì would add
+  no speed, capability, or reliability benefit to the model already running here, and its own numbers
+  show it isn't fast enough to serve the interactive coding-CLI use case this stack exists for.
+- It could only ever be interesting as a *bolt-on, offline-only* capability for occasionally running an
+  otherwise-impossible frontier-scale model (700B–2.8T params) for patient, non-interactive tasks — at
+  the cost of hundreds of GB to ~1.6TB of extra disk per model, on a ~10-week-old, single-maintainer,
+  still-hardening project (two security-patch releases already) with no ROCm-specific documentation and
+  the thinnest testing history of its four GPU backends.
+- **Recommendation: worth a passing watch, not worth integrating.** Revisit if/when: (a) the project
+  reaches a more established maturity point (6–12 months, broader contributor base, dedicated ROCm docs
+  bringing it to parity with the CUDA/Metal paths), and (b) there's an actual concrete need in this stack
+  to run a model in the 200B+ range that cannot fit on the R9700 — which isn't the case today (the
+  current model is deliberately sized to fit fully in VRAM). Until then, integrating it would add
+  operational surface (a new container, huge disk provisioning, a newer/less-hardened codebase) for no
+  measurable improvement over the existing llama.cpp/ROCm path.

scripts/switch-model.sh

@@ -11,12 +11,6 @@
 # it never reliably sleeps a service on its own. A scripted swap always
 # knows which service should go up/down, so it doesn't need that heuristic.
 #
-# llama-server-fast (the small classifier model, issue #44) is NOT part of
-# this swap — it's meant to stay always-resident. Worst case with comfyui up
-# is comfyui (~25GB, Qwen-Image FP8) + llama-server-fast (~5GB) ≈ 30GB,
-# still under the 32GB card but tight — unverified on real hardware, check
-# `docker compose ps` / VRAM usage after the first real swap.
-#
 # OmniRoute may show the just-stopped provider as errored/offline in its
 # dashboard for up to CREDENTIAL_HEALTH_CHECK_INTERVAL (default 5 min) after
 # a swap — cosmetic, not a functional problem (see the research doc above).