# deploy-vllm

Idempotent Ansible role that deploys a vLLM OpenAI-compatible inference
server. Written for astro-orbiter (RTX 3090, 24GB VRAM, 64GB RAM, Ubuntu
24.04) and designed for reuse on the planned Mac Mini M4 host later this
week (see "Portability" below).

Supersedes the manual, pre-role state left behind by earlier vLLM
experiments (`/home/jarvis/vllm-env`, bitsandbytes, gemma-2-27b — see
`homelab-llm-inference`/`homelab-llm-serving` skills for that history). This
role uses a **fresh venv** (`vllm_venv_path`, default `~/vllm-serve-env`) and
**AWQ pre-quantized models** — no bitsandbytes, no on-the-fly quantization,
no repeat of the OOM incident from the earlier Gemma-2-27B attempt.

## Phases

| Phase | File | What it does |
|---|---|---|
| 1 | `tasks/dependencies.yml` | System Python 3.10+, dedicated venv, `pip install vllm>=0.5.0`, verifies `nvidia-smi` and `torch.cuda.is_available()` |
| 2 | `tasks/models.yml` | Downloads each `enabled: true` model in `vllm_models` via `hf download` (huggingface_hub CLI) into `~/.vllm-cache`, verifies the snapshot landed and reports on-disk size |
| 3 | `tasks/api-key.yml` | Reads the API key from 1Password (`op://mk-labs/vllm/api-key`) on the **controller**, writes it to `/etc/vllm/api-key.env` (root:root, 0600) on the target |
| 4 | `tasks/systemd.yml` | Renders and installs one systemd unit per enabled model (`vllm.service` for the `role: primary` model, `vllm-<id>.service` for others) |
| 5 | `tasks/verify.yml` | Only runs when `vllm_service_state=started`. Waits for `/health` (up to 5 min — torch.compile warmup), checks `/v1/models`, runs a live completion, scans `journalctl` for errors |

Run all phases: `ansible-playbook -i inventory.yml playbooks/day1_deploy_vllm.yml --limit astro-orbiter`
Run one phase: `--tags vllm-dependencies` / `vllm-models` / `vllm-api-key` / `vllm-systemd` / `vllm-verify`

## Deliberate staging-first default

`vllm_service_state` defaults to `stopped`. A default run **stages
everything** (venv, model weights, API key file, systemd unit) but does
**not** start the service or touch production traffic. This matches the
astro-orbiter cutover plan: llama-swap is live production serving (Qwen3.8-27B
+ nomic-embed for Hindsight) — vLLM must be deployed and validated on a
side port/inactive unit before anything is cut over.

To start and validate:

```bash
ansible-playbook -i inventory.yml playbooks/day1_deploy_vllm.yml \
  --limit astro-orbiter --extra-vars "vllm_service_state=started"
```

This starts the systemd unit(s), enables them, and runs Phase 5 verification
(health, `/v1/models`, live completion, clean journalctl).

**Cutover of consumers (Hermes profiles, Hindsight embedding config, any
hardcoded `:8001`/`:5805` references) to the new `:8000` vLLM endpoint is a
separate, explicit step outside this role** — do this only after Phase 5
passes cleanly. Do not tear down llama-swap until consumers are confirmed
working end-to-end against vLLM.

## Model roster (`vllm_models` in defaults/main.yml)

vLLM 0.5.x-0.28.x serves **one model per process** — multi-model = multiple
systemd units on distinct ports, not a single multiplexed server (unlike
llama-swap's matrix DSL). Today's phase enables only the primary model;
flip `enabled: true` on the others as VRAM allows (see "Phased Strategy"):

| id | hf_repo | role | port | quant | enabled |
|---|---|---|---|---|---|
| Qwen2.5-32B-Instruct-AWQ | Qwen/Qwen2.5-32B-Instruct-AWQ | primary | 8000 | awq | **true** |
| Qwen3-8B-AWQ | Qwen/Qwen3-8B-AWQ | aux | 8010 | awq | false |
| nomic-embed-text-v1.5 | nomic-ai/nomic-embed-text-v1.5 | embedding | 8020 | none | false |

**Note on the original spec's model choices:** the task body named
`Qwen/Qwen2.5-32B-Instruct` and `Qwen/Qwen3-8B-Instruct` (bf16, unquantized).
vLLM does not do on-the-fly quantization safely on this host (bitsandbytes
OOM history — see `homelab-llm-inference` skill Pitfalls) and unquantized
bf16 32B does not fit a 24GB card at all (~65GB). This role instead deploys
the **official Qwen AWQ pre-quantized variants**
(`Qwen/Qwen2.5-32B-Instruct-AWQ`, `Qwen/Qwen3-8B-AWQ`), which vLLM natively
supports (`--quantization awq`) and which fit the VRAM budget:

- Qwen2.5-32B-Instruct-AWQ: ~19.3GB on disk, fits with ~5GB headroom at 24GB
- Qwen3-8B-AWQ: ~6GB VRAM per llm-explorer
- nomic-embed-text-v1.5: ~300MB, vLLM serves it via `--convert embed` pooling
  (see vLLM embedding docs) — **not yet wired into this role's systemd
  template**; the embedding model needs `--task embed` / `--convert embed`
  flags that differ from the completion-serving template. Flagged as a
  follow-up before `enabled: true` is flipped on it (see Known Gaps below).

## Known Gaps / Follow-ups

- Quarterly API key rotation is documented (`/etc/vllm/API_KEY_ROTATION.md`
  on the target, rendered by `tasks/api-key.yml`) but not automated — no cron
  job exists to force rotation on a schedule. Consider a follow-up cron task
  if Nick Fury wants this enforced rather than just documented.
- `vllm_service_enabled` defaults to `false` deliberately — see "Deliberate
  staging-first default" above. Flip together with the cutover step, not
  before.
- **vLLM cannot replace llama-swap's full model roster on this card (t_e6facb19, 2026-08-31).**
  This role only wires two of llama-swap's five served models: the primary
  completions model (Qwen2.5-32B-Instruct-AWQ, replacing Qwen3.8-27B) and
  the embedding model (nomic-embed-text-v1.5). llama-swap ALSO serves
  Qwen3-8B-Q4_K_M(-no_think), Phi-3.5-mini-instruct-Q8_0,
  Meta-Llama-3.1-8B-Instruct-Q4_K_M, and Qwen2.5-Coder-14B-Instruct-Q4_K_M —
  21 Hermes agent profiles' `custom_providers` reference these model IDs for
  aux tasks (skills_hub, approval, mcp, title_generation, profile_describer,
  compression). vLLM 0.28 serves **one model per process**; running 5-6
  separate vLLM processes concurrently does not fit a 24GB card (each
  process reserves its own CUDA context + weights + KV cache, unlike
  llama-swap's matrix DSL which time-shares one GPU across LRU-evicted
  processes). **Full llama-swap teardown (t_6dff1ecc) cannot proceed until
  either:** (a) the aux-model consumers are migrated to a different backend
  (Anthropic, or a smaller local llama.cpp router kept alongside vLLM), or
  (b) vLLM gains a comparable multi-model time-sharing mode. This is a
  genuine architecture gap, not a missing role feature — flagging for a
  human decision on the aux-model strategy before teardown can be
  unconditionally safe.

## Embedding-mode support (t_e6facb19, 2026-08-31)

`vllm.service.j2` now branches on `role: embedding` entries in `vllm_models`:
adds `--runner pooling --convert embed` (vLLM's embedding-serving flags —
see https://docs.vllm.ai/en/latest/models/pooling_models/embed/) and
`--no-enable-prefix-caching` (prefix caching is a completions-only
optimization; irrelevant and safely disabled for pooling). An additional
per-model `trust_remote_code: true` toggle renders `--trust-remote-code`
when set — required for `nomic-ai/nomic-embed-text-v1.5`, which ships
custom `NomicBertModel` modeling code on its HF repo.

**Verification does NOT run `/v1/completions` against embedding-mode
instances** (they don't serve that endpoint — a completions request 400s
immediately). `tasks/verify.yml` splits `vllm_enabled_models` by `role` and
runs the appropriate smoke test per group: completions models get the
`/v1/completions` "capital of France" test; embedding models get a real
`/v1/embeddings` POST with an `ansible.builtin.assert` on a non-empty
`data[0].embedding` array (not just HTTP 200 — an empty/malformed vector
would still 200).

**Critical VRAM finding: co-resident completions + embedding vLLM processes
need MORE headroom than either alone, and CUDA graph capture is the failure
mode, not KV cache sizing.** Enabling `nomic-embed-text-v1.5` alongside the
primary Qwen2.5-32B model at the role-default `gpu_memory_utilization: 0.95`
crash-looped repeatedly:
- First failure: `torch.OutOfMemoryError` during `capture_model()` (CUDA
  graph capture) — KV cache sizing itself succeeded (14,720 tokens
  allocated), but graph capture needed ~20MiB more than the 0.95 budget had
  left once nomic's embedding process (814MiB actual, not the nominal
  ~300MB estimate in the model roster table) claimed its share.
- Fix attempt 1: added a per-model `enforce_eager: true` template branch
  (`--enforce-eager` skips CUDA graph capture entirely) — this stopped the
  graph-capture OOM but the combined processes still landed at only
  ~847MiB genuinely free out of 24,576MiB, and both services crash-looped
  6-7 times during warmup before finally stabilizing (each attempt leaves
  transient VRAM that the next attempt fights over, extending time-to-stable
  well past a single health-check retry window).
- Fix attempt 2 (final, verified stable): lowered the primary model's
  `gpu_memory_utilization` from 0.95 to **0.90** (host_vars override) in
  addition to `enforce_eager: true`. Result: clean single-attempt start for
  both services, `NRestarts=0`, ~2GB genuinely free (22,577MiB used /
  24,576MiB total). Confirmed via `systemctl show <unit> -p NRestarts` after
  a full stop/start cycle — 0.95 was NOT a fluke of Restart=always masking
  the underlying fragility; 0.90 is a real, reproducible fix.
- **Takeaway for future multi-process vLLM VRAM budgeting on this host:**
  do not just check "does it eventually come up" — check `NRestarts` and
  free VRAM headroom after a clean stop/start. A model that "works" after
  6 crash-loop retries is not production-stable; the retries themselves are
  evidence the utilization ceiling is too tight for the actual (not
  nominal) footprint of co-resident processes.

## Consumer cutover status (t_e6facb19, 2026-08-31)

**Cut over (validated end-to-end):**
- Hindsight (`cluster/applications/hindsight/values.yaml` +
  `externalsecret.yaml`): `HINDSIGHT_API_LLM_BASE_URL` → vLLM `:8000`
  (Qwen2.5-32B-Instruct-AWQ), plus newly-wired `HINDSIGHT_API_EMBEDDINGS_*`
  env vars pointing at vLLM `:8020` (nomic-embed-text-v1.5). Both endpoints
  require vLLM's real API key (unlike llama-swap, which accepted
  any/no key) — ExternalSecret now reads `op://mk-labs/vllm/api-key`
  (item "vllm") instead of the prior Nous fallback item, and reuses the same
  key value for `HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY` (both vLLM
  endpoints share one key file per `tasks/api-key.yml`).

**NOT cut over — genuine scope gap requiring a human decision, see
"Known Gaps" above:** the 21 Hermes agent profiles' aux-model
`custom_providers` entries (Qwen3-8B-no_think, Phi-3.5-mini, Meta-Llama-3.1-8B,
Qwen2.5-Coder-14B) still point at llama-swap `:8001` — vLLM has no
equivalent multi-model serving mode on this 24GB card. llama-swap MUST stay
up to serve these until that gap is resolved. This is why the teardown task
(t_6dff1ecc) remains blocked even after this task's completion — see the
comment posted there.

- OpenViking (`cluster/platform/openviking/values.yaml`): still points at
  llama-swap `:8001` (`Meta-Llama-3.1-8B-Instruct-Q4_K_M` VLM + nomic-embed
  for dense embeddings). Left unchanged — its VLM model has no vLLM
  equivalent staged, and migrating only its embedding path while leaving its
  VLM on llama-swap would still require llama-swap up, providing zero
  teardown benefit. Flagged, not touched, per the same aux-model gap above.


## Validation Log (2026-08-31, t_ca1af9fb)

Full Phase 1-5 run executed against astro-orbiter in a brief shadow-validation
window (llama-swap stopped ~5 min, per the `homelab-llm-inference` skill's
documented shadow-validation pattern — production traffic could not be
tested concurrently with vLLM's VRAM footprint on this 24GB card).

**Two real bugs found and fixed during first-start validation** (not present
in the original spec, discovered only by actually starting the service):

1. **`ninja` not on systemd's PATH.** vLLM's torch.compile path shells out to
   the bare `ninja` command. `pip install vllm` installs `ninja` (and its
   console-script entrypoint) into the venv's `bin/`, but systemd's minimal
   default PATH doesn't include that directory — `FileNotFoundError: 'ninja'`
   only reproduces under systemd, not interactive SSH testing. Fixed by
   setting `Environment="PATH=<venv>/bin:...standard dirs..."` in the unit
   template.
2. **FlashInfer sampler JIT fails to compile on RTX 3090 (SM86).**
   `flashinfer/data/csrc/sampling.cu` uses a cub template API
   (`BlockAdjacentDifference::FlagHeads`) not present in this
   flashinfer/CUDA-toolkit combination — 100 compile errors, confirmed as a
   known upstream issue class (vLLM GH #23023, #44305: FlashInfer sampler JIT
   breaking on various SM targets). Fixed with
   `Environment="VLLM_USE_FLASHINFER_SAMPLER=0"`, falling back to vLLM's
   native PyTorch sampler (fully supported, negligible perf difference at
   single-request serving volume).

Also corrected `vllm_gpu_memory_utilization` from 0.90 to 0.95 — at 0.90 the
KV cache allocation failed (`2.0 GiB KV cache needed, 1.3 GiB available`)
even with the full 24GB card free, because 32B AWQ weights alone consume
~18.4GB, leaving too little headroom at a 90% cap.

**Idempotency bug also found and fixed:** upgrading `setuptools` to "latest"
in Phase 1 fought with vLLM's own `setuptools<81.0.0` pin, causing a
install/downgrade flip-flop (`changed: true`) on every single run. Fixed by
removing setuptools from the explicit-upgrade list and letting vLLM's own
`pip install` resolve it.

**Final validated result, once these fixes were applied:**
- `systemctl status vllm.service` → active, clean journalctl (no
  error/traceback lines) after the successful start
- `curl /health` → HTTP 200
- `curl /v1/models` → returns `Qwen2.5-32B-Instruct-AWQ`
- `curl /v1/completions` → live completion returned correct output
  (`"The capital of France is" → " Paris. Correct! The capital of France"`)
- Second and third full-role runs (`vllm_service_state` default, `stopped`)
  → `changed=0` both times — confirmed idempotent
- Production restored: `llama-swap.service` active, `/health` 200,
  `/v1/embeddings` against `nomic-embed-text-v1.5` returns a valid vector —
  Hindsight retain path confirmed still working after the shadow window
- Post-restore VRAM: 486 MiB used / 24,576 MiB total (normal quiescent state)

## Testing this role (idempotency)

Second-run test (staging phases only, safe to run repeatedly):

```bash
ansible-playbook -i inventory.yml playbooks/day1_deploy_vllm.yml \
  --limit astro-orbiter --tags vllm-dependencies,vllm-models,vllm-api-key,vllm-systemd
# Run it again immediately — expect changed=0 (or only handler-driven
# restarts if vllm_service_state=started and the API key file rotated)
```

Confirmed 2026-08-31 (t_ca1af9fb): Phase 1 (dependencies) ran once with
changed=3 (venv create, pip upgrade, vllm install); a second run reported
changed=0 for those three tasks — venv `creates:` guard and pip module's
own idempotency both held.

## Portability — Mac Mini M4 (planned, end of week)

This role's host-specific assumptions live in `defaults/main.yml` (all
overridable via `host_vars/<host>/vars.yml`) plus one hard assumption baked
into `tasks/dependencies.yml`: an NVIDIA GPU (`nvidia-smi` check, CUDA
wheels). Apple Silicon has **no CUDA** — vLLM's Metal/MPS backend support is
immature as of this writing. Before reusing this role for the Mac Mini M4:

1. Fork `tasks/dependencies.yml`'s GPU-check + CUDA-wheel-install logic into
   a platform-conditional block (`when: ansible_facts.system == 'Darwin'`
   branch installing the CPU/MPS vLLM wheel, or MLX-based serving instead —
   needs a decision before that work starts, not assumed here).
2. `vllm_venv_owner`, `vllm_serve_port`, `vllm_models` are already host_vars-
   driven — no changes needed there.
3. systemd unit templates assume a Linux init system — macOS needs a
   launchd plist instead of `vllm.service.j2`.

This is flagged as a distinct follow-up task, not solved in this role —
scope for this deployment was astro-orbiter only, per the task body's
"Phased Strategy: ... End of week: Mac Mini M4 variant" (a separate future
pass, not blocking this completion).

## Files

```
roles/deploy-vllm/
├── defaults/main.yml               # all tunables — host overrides go in host_vars
├── handlers/main.yml                # reload systemd / restart vllm services
├── meta/main.yml
├── tasks/
│   ├── main.yml                     # phase orchestrator
│   ├── dependencies.yml             # Phase 1
│   ├── models.yml                   # Phase 2
│   ├── api-key.yml                  # Phase 3
│   ├── systemd.yml                  # Phase 4
│   └── verify.yml                   # Phase 5
├── templates/
│   ├── vllm.service.j2              # one instance per enabled model
│   └── vllm-workspace.sh.j2         # debugging helper deployed to the target
└── README.md                        # this file
```