ocr: size a VLM engine's vLLM against free VRAM, and never answer 503 (v0.2.25)

surya-on-vLLM was failing every ~20 minutes in production with
"Available KV cache memory: -2.81 GiB", and the cause was the 0.35 share itself.
vLLM's --gpu-memory-utilization is a fraction of the card's TOTAL memory and every
other process' allocation counts against it, so with 3.6 GB of resident bge-m3 on a
24 GB 3090 a 0.35 budget left surya about 5 GB — less than its weights (1.37) plus
activation peak at max_model_len=18432 plus CUDA graphs (0.30). Each attempt burned
~3 minutes of boot and profiling, then died, and the next OCR request started over.

- vlm_gpu_memory_utilization is now the engine's OWN share and is translated at launch
  against what else holds the card (effective_gmu): 0.35 with 3.6 GB resident becomes
  0.50, i.e. a 12 GB budget of which 8.4 GB is the engine's. Capped at 0.92 so the
  driver keeps its room.
- The evict-first demand (prelaunch_free_gb) now covers the share plus that headroom,
  so a genuinely crowded card gets models evicted rather than the share clamped.
- stop_service_for reports what the instance actually reserved, recorded at launch,
  instead of the caller's share — under-reporting sent the evictor hunting for VRAM
  that was already free.
- Stopping the managed vLLM is no longer gated on the engine's current serve mode: a
  mode changed while a service was up leaked its share of the card until a restart.
- ocr.fallback_engines (default "auto"): an engine that cannot serve hands the document
  to the other enabled engines instead of returning 503, fewest-ways-to-fail first. The
  response's "engine" field names whoever actually read the pages. This also covers a
  pool evicted mid-document — the failed pages are re-read rather than failing.

21 new tests; the two older vLLM-worker tests now patch the package attribute as well
as sys.modules, without which the fake worker silently did not apply.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0182DhR1eFNPQyGmDrpHbedY

codai/__init__.py

@@ -16,7 +16,7 @@
 
 # Canonical product version for CoderAI — single source of truth. Both the API
 # metadata and the admin web UI read from here.
-__version__ = "0.2.24"
+__version__ = "0.2.25"
 __tagline__ = "Complete orchestration, distribution and escalation of remotizable advanced inference."
 
 # Configure the CUDA caching allocator BEFORE torch is imported anywhere.

codai/admin/routes.py

@@ -3872,6 +3872,7 @@ def build_settings_dict(c, gpu_cards):
             "surya_server_url": c.ocr.surya_server_url,
             "build_retry_cooldown_s": c.ocr.build_retry_cooldown_s,
             "vlm_gpu_memory_utilization": c.ocr.vlm_gpu_memory_utilization,
+            "fallback_engines": c.ocr.fallback_engines,
             "olmocr_enabled": c.ocr.olmocr_enabled,
             "olmocr_instances": c.ocr.olmocr_instances,
             "olmocr_serve": c.ocr.olmocr_serve,
@@ -4603,6 +4604,7 @@ async def api_save_settings(request: Request, username: str = Depends(require_ad
         if "surya_server_url" in d: o.surya_server_url = (d.get("surya_server_url") or "").strip()
         if "build_retry_cooldown_s" in d: o.build_retry_cooldown_s = _f("build_retry_cooldown_s", o.build_retry_cooldown_s, 0.0)
         if "vlm_gpu_memory_utilization" in d: o.vlm_gpu_memory_utilization = _f("vlm_gpu_memory_utilization", o.vlm_gpu_memory_utilization, 0.0)
+        if "fallback_engines" in d: o.fallback_engines = (d.get("fallback_engines") or "auto").strip()
         if "olmocr_enabled" in d: o.olmocr_enabled = bool(d["olmocr_enabled"])
         if "olmocr_instances" in d: o.olmocr_instances = _i("olmocr_instances", o.olmocr_instances, 1)
         if "olmocr_serve" in d: o.olmocr_serve = (d.get("olmocr_serve") or "model").strip().lower()

codai/admin/templates/settings.html

@@ -1074,6 +1074,12 @@
         <label class="form-label">VLM engines' GPU share</label>
         <input type="number" id="s-ocr-vlm-gmu" class="form-input" min="0" max="1" step="0.05" placeholder="0 = vLLM default">
       </div>
+      <div class="form-group" style="flex:1;min-width:170px">
+        <label class="form-label">Fallback engines</label>
+        <input type="text" id="s-ocr-fallback" class="form-input" placeholder="auto">
+        <div class="form-hint">Tried when the chosen engine can't serve, so a document
+          is never refused: <code>auto</code>, <code>off</code>, or an explicit order.</div>
+      </div>
     </div>
 
     <div class="card-title" style="font-size:13px;margin-top:.4rem">PaddleOCR</div>
@@ -2004,6 +2010,7 @@ async function loadSettings(){
     document.getElementById('s-ocr-lang').value = ocr.lang ?? 'it';
     document.getElementById('s-ocr-retry-cooldown').value = ocr.build_retry_cooldown_s ?? 60;
     document.getElementById('s-ocr-vlm-gmu').value = ocr.vlm_gpu_memory_utilization ?? 0;
+    document.getElementById('s-ocr-fallback').value = ocr.fallback_engines ?? 'auto';
     document.getElementById('s-ocr-paddle-enabled').checked = ocr.paddle_enabled !== false;
     document.getElementById('s-ocr-paddle-use-gpu').checked = ocr.paddle_use_gpu !== false;
     document.getElementById('s-ocr-paddle-structure').checked = ocr.paddle_structure !== false;
@@ -2231,6 +2238,7 @@ async function saveSettings(silent){
       lang: document.getElementById('s-ocr-lang').value.trim() || 'it',
       build_retry_cooldown_s: parseFloat(document.getElementById('s-ocr-retry-cooldown').value) || 0,
       vlm_gpu_memory_utilization: parseFloat(document.getElementById('s-ocr-vlm-gmu').value) || 0,
+      fallback_engines: document.getElementById('s-ocr-fallback').value.trim() || 'auto',
       paddle_enabled: document.getElementById('s-ocr-paddle-enabled').checked,
       paddle_use_gpu: document.getElementById('s-ocr-paddle-use-gpu').checked,
       paddle_structure: document.getElementById('s-ocr-paddle-structure').checked,

codai/api/vllm_worker.py

@@ -125,9 +125,7 @@ def _launch_cmd(py, cfg, host: str, port: int, model_path: str,
     ctx = int(getattr(cfg, "ctx", 0) or 0)
     if ctx > 0:
         cmd += ["--max-model-len", str(ctx)]
-    # A side job that serves its OWN small model on a shared card (the OCR VLM engines)
-    # passes its own share here, so it does not have to claim as much of the GPU as the
-    # LLM instance wants.
+    # Already absolute (resolve_gmu translated a side job's own-share if there was one).
     gmu = float(gpu_memory_utilization or 0) or float(getattr(cfg, "gpu_memory_utilization", 0) or 0)
     if gmu > 0:
         cmd += ["--gpu-memory-utilization", str(gmu)]
@@ -302,9 +300,13 @@ def ensure_service(cfg, model_path: Optional[str] = None,
         port = int(getattr(cfg, "port", 0) or 0) or _free_port()
         url_host = "127.0.0.1" if host in ("0.0.0.0", "") else host
         url = f"http://{url_host}:{port}"
+        # Resolve the share HERE and remember it: what this instance actually reserved is
+        # what stopping it frees, and the eviction path has to be told the truth or it
+        # over- or under-evicts for the next model.
+        gmu = resolve_gmu(cfg, gpu_memory_utilization)
         cmd = _launch_cmd(py, cfg, host, port, model, served_name,
                           model_config=_model_config_for(resolved or model),
-                          gpu_memory_utilization=gpu_memory_utilization)
+                          gpu_memory_utilization=gmu)
 
         env = os.environ.copy()
         # flashinfer JIT-compiles CUDA kernels with ninja at runtime, which fails on
@@ -340,7 +342,8 @@ def ensure_service(cfg, model_path: Optional[str] = None,
                 ray.stop()
             raise
         threading.Thread(target=_pump_logs, args=(proc, tail), daemon=True).start()
-        _services[svc_key] = {"proc": proc, "port": port, "url": url, "ray": ray}
+        _services[svc_key] = {"proc": proc, "port": port, "url": url, "ray": ray,
+                              "gmu": gmu}
 
     def _tail_msg():
         # The last six lines of a vLLM crash are the wrapper's own traceback
@@ -412,6 +415,94 @@ def is_running(cfg, model_path: Optional[str] = None, served_name: Optional[str]
         return bool(svc and svc["proc"].poll() is None)
 
 
+# vLLM must not be told to budget the whole card: the driver context, fragmentation and
+# any allocation made between our measurement and its own need somewhere to live.
+GMU_CEILING = 0.92
+
+
+def _used_by_others_gb() -> float:
+    """VRAM (GB) currently held on this box by anything that is NOT the vLLM we are
+    about to launch — resident embedders, an LLM, another process' context."""
+    try:
+        from codai.models.manager import multi_model_manager
+        total = float(multi_model_manager._total_vram_gb() or 0.0)
+        free = float(multi_model_manager._get_free_vram_gb() or 0.0)
+        if total <= 0 or free >= 999.0:   # 999 = "unknown, assume enough"
+            return 0.0
+        return max(0.0, total - free)
+    except Exception:
+        return 0.0
+
+
+def resolve_gmu(cfg, gpu_memory_utilization: Optional[float] = None) -> float:
+    """The absolute ``--gpu-memory-utilization`` to launch with.
+
+    A side job (an OCR VLM engine) passes the share it wants for ITSELF, which has to be
+    translated against what else is on the card; the backend's own configured value is
+    already absolute and is used as-is."""
+    share = float(gpu_memory_utilization or 0)
+    if share > 0:
+        return effective_gmu(share)
+    return float(getattr(cfg, "gpu_memory_utilization", 0) or 0)
+
+
+def effective_gmu(self_share: float) -> float:
+    """Translate "the share of the card I want for MYSELF" into the absolute
+    ``--gpu-memory-utilization`` vLLM wants.
+
+    vLLM's flag is a fraction of the card's TOTAL memory and every other process'
+    allocation counts against it: with 3.6 GB of bge-m3 resident on a 24 GB 3090,
+    ``0.35`` left surya-2 about 5 GB, and after weights (1.37) + activation peak at
+    ``max_model_len=18432`` + CUDA graphs (0.3) it reported ``Available KV cache memory:
+    -2.81 GiB`` and died — every 20 minutes, for hours. So add back what others hold:
+    0.35 of a card with 3.6 GB in use becomes 0.50, i.e. a 12 GB budget of which 8.4 GB
+    is actually ours. Capped at :data:`GMU_CEILING`; when the cap bites, the caller's
+    evict-first pass (``prelaunch_vram_gb``) is what makes room."""
+    try:
+        share = max(0.0, float(self_share or 0.0))
+    except Exception:
+        return 0.0
+    if share <= 0:
+        return 0.0
+    try:
+        from codai.models.manager import multi_model_manager
+        total = float(multi_model_manager._total_vram_gb() or 0.0)
+    except Exception:
+        total = 0.0
+    if total <= 0:
+        return min(share, GMU_CEILING)
+    used = _used_by_others_gb()
+    eff = share + (used / total)
+    if eff > GMU_CEILING:
+        print(f"[vllm] share {share:.2f} + {used:.1f} GB held by others exceeds the "
+              f"{GMU_CEILING:.2f} ceiling on a {total:.1f} GB card; clamping "
+              f"(eviction should have freed more)", flush=True)
+        eff = GMU_CEILING
+    if abs(eff - share) > 0.005:
+        print(f"[vllm] gpu-memory-utilization {share:.2f} (own share) -> {eff:.3f} "
+              f"absolute: {used:.1f} of {total:.1f} GB already in use", flush=True)
+    return eff
+
+
+def prelaunch_free_gb(cfg, gpu_memory_utilization: Optional[float] = None) -> float:
+    """How much VRAM must be FREE before launching this vLLM for the side-job share to
+    fit under :data:`GMU_CEILING` — i.e. what to ask the evictor for.
+
+    It is the engine's own need plus the headroom the ceiling reserves, so a crowded
+    card gets models evicted instead of :func:`effective_gmu` clamping and vLLM dying
+    on KV cache again."""
+    gmu = float(gpu_memory_utilization or 0) or float(getattr(cfg, "gpu_memory_utilization", 0) or 0)
+    if gmu <= 0:
+        return planned_vram_gb(cfg, gpu_memory_utilization)
+    try:
+        from codai.models.manager import multi_model_manager
+        total = float(multi_model_manager._total_vram_gb() or 24.0)
+    except Exception:
+        total = 24.0
+    total = total or 24.0
+    return max(0.5, (min(gmu, GMU_CEILING) + (1.0 - GMU_CEILING)) * total)
+
+
 def planned_vram_gb(cfg, gpu_memory_utilization: Optional[float] = None) -> float:
     """How much VRAM a vLLM instance started from ``cfg`` will claim on this box.
 
@@ -446,9 +537,21 @@ def stop_service_for(cfg, model_path: Optional[str] = None,
     with _lock:
         svc = _services.get(svc_key)
         running = bool(svc and svc["proc"].poll() is None)
+        launched_gmu = float((svc or {}).get("gmu") or 0.0)
     if not running:
         return 0.0
     stop_service(svc_key)
+    # What it reserved is what we just freed. The share the caller passes is the engine's
+    # OWN share; the instance was launched with that plus whatever else held the card
+    # (effective_gmu), so reporting the caller's figure would under-count the release and
+    # send the eviction path looking for memory that is already free.
+    if launched_gmu > 0:
+        try:
+            from codai.models.manager import multi_model_manager
+            total = float(multi_model_manager._total_vram_gb() or 24.0)
+        except Exception:
+            total = 24.0
+        return max(0.5, launched_gmu * (total or 24.0))
     return planned_vram_gb(cfg, gpu_memory_utilization)
 
 

codai/config.py

@@ -787,6 +787,13 @@ class OcrConfig:
     # utilization". 0.35 of a 24 GB card (8.4 GB) fits surya-2 / olmOCR-2 with their KV
     # cache and leaves the resident engines where they are.
     vlm_gpu_memory_utilization: float = 0.35
+    # When the chosen engine cannot serve a document, try these engines instead rather
+    # than failing the request: a 503 means a document nobody read, and that is strictly
+    # worse than a transcription from the second-best engine. "auto" = every other
+    # ENABLED engine, fewest-ways-to-fail first (paddle, doctr, olmocr, surya);
+    # "off"/"none" = no fallback (fail with the original error); or an explicit
+    # comma/space separated order.
+    fallback_engines: str = "auto"
 
     # --- PaddleOCR / PP-Structure ---
     paddle_enabled: bool = True
@@ -1417,6 +1424,7 @@ class ConfigManager:
                 "surya_server_url": self.config.ocr.surya_server_url,
                 "build_retry_cooldown_s": self.config.ocr.build_retry_cooldown_s,
                 "vlm_gpu_memory_utilization": self.config.ocr.vlm_gpu_memory_utilization,
+                "fallback_engines": self.config.ocr.fallback_engines,
                 "olmocr_enabled": self.config.ocr.olmocr_enabled,
                 "olmocr_instances": self.config.ocr.olmocr_instances,
                 "olmocr_serve": self.config.ocr.olmocr_serve,

codai/ocr/manager.py

@@ -282,8 +282,12 @@ class OcrManager:
     def _stop_surya_vllm(self) -> float:
         """Stop the managed Surya-2 vLLM subprocess (if this build serves Surya via vLLM).
         Returns estimated GB freed."""
+        # NOT gated on surya_serve being "vllm" right now: if the setting was changed
+        # while a service was up, the old subprocess is still holding its share of the
+        # card and skipping the stop would leak it until a restart. stop_service_for() is
+        # a no-op returning 0.0 when nothing is running, so asking is free.
         cfg = self._cfg
-        if cfg is None or (getattr(cfg, "surya_serve", "") or "").strip().lower() != "vllm":
+        if cfg is None:
             return 0.0
         try:
             from codai.api import vllm_worker
@@ -302,8 +306,8 @@ class OcrManager:
     def _stop_olmocr_vllm(self) -> float:
         """Stop the managed olmOCR vLLM instance (olmocr_serve = "vllm"), for the same
         reason Surya's needs stopping: its VRAM is the subprocess's, not a pool's."""
-        cfg = self._cfg
-        if cfg is None or (getattr(cfg, "olmocr_serve", "") or "").strip().lower() != "vllm":
+        cfg = self._cfg          # unconditional, for the reason in _stop_surya_vllm
+        if cfg is None:
             return 0.0
         try:
             from codai.api import vllm_worker
@@ -361,9 +365,27 @@ class OcrManager:
                 self._pools[name] = pool
             return pool
 
-    async def ocr_pages(self, images: List, engine: Optional[str] = None) -> (str, List[OcrPage]):
-        """OCR a list of PIL page images. Returns (engine_name, [OcrPage])."""
-        name = self.resolve_engine(engine)
+    def _fallback_order(self, failed: str) -> List[str]:
+        """Enabled engines to try after ``failed`` could not serve the document.
+
+        An OCR request that comes back 503 is a document nobody read — and the whole
+        point of having four engines is that a page can still be read when one of them
+        will not come up (a VLM engine whose vLLM cannot fit on the card, a venv that
+        needs rebuilding, a license flag). ``auto`` prefers the engines with the fewest
+        ways to fail over the ones with the best output, because the alternative to a
+        worse transcription here is no transcription."""
+        cfg = self._require_cfg()
+        raw = str(getattr(cfg, "fallback_engines", "auto") or "auto").strip().lower()
+        if raw in ("off", "none", "no", "false"):
+            return []
+        if raw in ("auto", "", "on", "true"):
+            names = ["paddle", "doctr", "olmocr", "surya"]
+        else:
+            names = [n.strip() for n in raw.replace(",", " ").split() if n.strip()]
+        return [n for n in names
+                if n != failed and n in _ENGINE_FACTORIES and _engine_enabled(cfg, n)]
+
+    async def _pages_with(self, name: str, images: List) -> List[OcrPage]:
         pool = await self._get_pool(name)
         sem = self._sem or asyncio.Semaphore(4)
 
@@ -374,7 +396,35 @@ class OcrManager:
                 return page
 
         pages = await asyncio.gather(*[_one(i, im) for i, im in enumerate(images)])
-        return name, list(pages)
+        return list(pages)
+
+    async def ocr_pages(self, images: List, engine: Optional[str] = None) -> (str, List[OcrPage]):
+        """OCR a list of PIL page images. Returns (engine_name, [OcrPage]).
+
+        The returned name is the engine that ACTUALLY read the pages, which is not
+        necessarily the one asked for: when an engine cannot serve, the remaining
+        enabled engines are tried in turn (see :meth:`_fallback_order`) rather than
+        failing the document. Callers surface the name, so a caller that cares can see
+        it fell back."""
+        name = self.resolve_engine(engine)
+        try:
+            return name, await self._pages_with(name, images)
+        except Exception as first:
+            chain = self._fallback_order(name)
+            if not chain:
+                raise
+            print(f"[ocr] engine '{name}' could not serve ({first}); "
+                  f"falling back to {' -> '.join(chain)}")
+            for alt in chain:
+                try:
+                    pages = await self._pages_with(alt, images)
+                except Exception as e:
+                    print(f"[ocr] fallback engine '{alt}' also failed: {e}")
+                    continue
+                print(f"[ocr] served by fallback engine '{alt}' instead of '{name}'")
+                return alt, pages
+            # Everything enabled is down: the original failure is the useful one.
+            raise first
 
     async def ocr_document(self, data: bytes, filename: str = "", content_type: str = "",
                            engine: Optional[str] = None, dpi: Optional[int] = None,

codai/ocr/olmocr.py

@@ -276,11 +276,14 @@ class OlmOcrEngine(OcrEngine):
         return v or None
 
     def _vllm_need(self) -> float:
+        # The engine's own share PLUS the headroom effective_gmu's ceiling reserves: our
+        # share is translated against whatever else holds the card, so a crowded card has
+        # to be cleared rather than clamped.
         try:
-            from codai.api.vllm_worker import planned_vram_gb
+            from codai.api.vllm_worker import prelaunch_free_gb
             from codai.models.manager import get_active_vllm_config
             vcfg = get_active_vllm_config()
-            return planned_vram_gb(vcfg, self._vlm_gmu()) if vcfg is not None else 0.0
+            return prelaunch_free_gb(vcfg, self._vlm_gmu()) if vcfg is not None else 0.0
         except Exception:
             return 0.0
 

codai/ocr/surya.py

@@ -53,15 +53,18 @@ class SuryaEngine(SubprocessOcrEngine):
     def prelaunch_vram_gb(self) -> float:
         """In the served modes Surya rides a vLLM instance that grabs
         gpu_memory_utilization × the whole card before it will start, so that much has to
-        be free FIRST. (A 3090 with 11.3 GB free against a 14.1 GB demand crash-looped
-        Surya-2 for hours: vLLM died on startup, the next OCR request tried again.)"""
+        be free FIRST — plus the ceiling's headroom, since our share is translated against
+        what others hold (see vllm_worker.effective_gmu). (A 3090 with 11.3 GB free against
+        a 14.1 GB demand crash-looped Surya-2 for hours: vLLM died on startup, the next OCR
+        request tried again. Then 3.6 GB of resident embedder did the same to a 0.35
+        share.)"""
         if self._serve_mode() != "vllm":
             return 0.0
         try:
-            from codai.api.vllm_worker import planned_vram_gb
+            from codai.api.vllm_worker import prelaunch_free_gb
             from codai.models.manager import get_active_vllm_config
             vcfg = get_active_vllm_config()
-            return planned_vram_gb(vcfg, self._vlm_gmu()) if vcfg is not None else 0.0
+            return prelaunch_free_gb(vcfg, self._vlm_gmu()) if vcfg is not None else 0.0
         except Exception:
             return 0.0
 

docs/ocr.md

@@ -121,11 +121,21 @@ thing out there.
 
 The VLM engines (`surya` in `vllm` mode, `olmocr` in `vllm` mode) boot a vLLM instance
 that claims `gpu_memory_utilization` × the **whole card** before it will serve, and
-refuses to start when that much is not free. Two rules follow, both enforced by the pool:
+refuses to start when that much is not free. Three rules follow, all enforced by the pool:
 
 - **Room is made before the load, not after it.** The engine declares what it needs up
   front (`prelaunch_vram_gb`) and the model manager evicts for it first. Evicting
   afterwards is useless, because the load is what fails.
+- **The share is relative to what is free, not to the card.** `vlm_gpu_memory_utilization`
+  is the slice the OCR engine wants for *itself*, but vLLM's own flag is a fraction of
+  **total** card memory and every other process' allocation counts against it. So the
+  share is translated at launch: 0.35 on a 24 GB card with 3.6 GB of resident embedder
+  becomes `--gpu-memory-utilization 0.50`, a 12 GB budget of which 8.4 GB is actually the
+  engine's. Without the translation, 0.35 left surya-2 about 5 GB, and after weights,
+  activation peak at `max_model_len=18432` and CUDA graphs it reported
+  `Available KV cache memory: -2.81 GiB` and died — once every 20 minutes, for hours.
+  The translation is capped (0.92) so the driver keeps its room; when the cap would bite,
+  the evict-first pass above is what clears the card instead.
 - **A failed build is not retried on every request.** For `ocr.build_retry_cooldown_s`
   (default 60 s) that engine fails fast with the reason the boot gave, instead of spending
   ~35 s booting and dying again per request. Without this, a card 11 GB short of Surya-2's
@@ -140,11 +150,23 @@ of 0.9 (or 0.6) can be more than the card has left. Otherwise: give the card les
 resident work, or serve the engine with `model`/`server` mode, which needs no instance of
 its own at all.
 
+### A document is never refused because one engine is down
+
+`ocr.fallback_engines` (default `auto`) makes a failing engine hand the document to the
+other **enabled** engines rather than returning 503 — fewest-ways-to-fail first
+(`paddle`, `doctr`, `olmocr`, `surya`), since a transcription from the second-best engine
+beats no transcription. The response's `engine` field names the engine that actually read
+the pages, so a fallback is visible to the caller, and the reason the first choice failed
+is logged. This also covers a pool that is evicted *mid-document* by a model load: the
+failed pages are re-read by the next engine instead of failing the request. Set an
+explicit order (`"doctr, paddle"`) to override it, or `off` to get the original error
+back.
+
 ## Enable & configure
 
 Settings → **OCR** card, or `config.json` `"ocr"`. Key fields: `enabled`,
 `default_engine`, `dpi`, `max_concurrency`, `lang`, `build_retry_cooldown_s`,
-`vlm_gpu_memory_utilization`; per-engine `*_enabled` /
+`vlm_gpu_memory_utilization`, `fallback_engines`; per-engine `*_enabled` /
 `*_instances` / `*_use_gpu` / lang; `detect_mode` (+ `detect_model_path`, `detect_conf`);
 `extract_enabled` / `extract_model_id` / `extract_schema` / `extract_validate`.
 

tests/test_ocr_olmocr.py

@@ -36,6 +36,18 @@ def _cfg(**kw):
     return c
 
 
+def _patch_vllm_worker(monkeypatch, fake):
+    """Swap in a fake vLLM worker for code that does ``from codai.api import vllm_worker``.
+
+    Patching sys.modules alone is not enough: once the real module has been imported by
+    anything (another test file, for instance), it is bound as an attribute of the
+    ``codai.api`` package and the from-import takes that attribute without consulting
+    sys.modules. Patch both, or the fake silently does not apply."""
+    import codai.api
+    monkeypatch.setitem(sys.modules, "codai.api.vllm_worker", fake)
+    monkeypatch.setattr(codai.api, "vllm_worker", fake, raising=False)
+
+
 # ------------------------------------------------------------ front matter
 def test_front_matter_split():
     meta, body = parse_front_matter(
@@ -312,19 +324,25 @@ def test_vllm_mode_serves_with_the_ocr_side_gpu_share(monkeypatch):
     """The OCR VLM gets its own share of the card, not the one sized for an LLM."""
     seen = {}
 
+    from codai.api.vllm_worker import GMU_CEILING
+    _headroom = 1.0 - GMU_CEILING      # the ceiling keeps room for the driver/fragmentation
+
     fake_worker = types.SimpleNamespace(
         ensure_service=lambda vcfg, model_path=None, served_name=None,
         gpu_memory_utilization=None: (
             seen.update(model=model_path, share=gpu_memory_utilization), "http://127.0.0.1:1/")[1],
         planned_vram_gb=lambda vcfg, share=None: (share or 0.9) * 24.0,
+        prelaunch_free_gb=lambda vcfg, share=None: ((share or 0.9) + _headroom) * 24.0,
     )
-    monkeypatch.setitem(sys.modules, "codai.api.vllm_worker", fake_worker)
+    _patch_vllm_worker(monkeypatch, fake_worker)
     monkeypatch.setattr("codai.models.manager.get_active_vllm_config",
                         lambda: types.SimpleNamespace(gpu_memory_utilization=0.9))
 
     cfg = _cfg(olmocr_serve="vllm", vlm_gpu_memory_utilization=0.35)
     eng = OlmOcrEngine(cfg)
-    assert eng.prelaunch_vram_gb() == pytest.approx(8.4)   # 0.35 of the card, not 0.9
+    # 0.35 of the card for itself (not the LLM's 0.9) PLUS the ceiling's headroom, because
+    # the share is translated against whatever else holds the card at launch.
+    assert eng.prelaunch_vram_gb() == pytest.approx((0.35 + _headroom) * 24.0)
     eng.load()
     assert seen["model"] == "allenai/olmOCR-2-7B-1025-FP8"
     assert seen["share"] == 0.35
@@ -335,7 +353,7 @@ def test_vllm_mode_serves_with_the_ocr_side_gpu_share(monkeypatch):
     assert OcrConfig().vlm_gpu_memory_utilization == 0.35
     # Set back to 0 it defers to whatever the vLLM backend itself is configured with.
     eng2 = OlmOcrEngine(_cfg(olmocr_serve="vllm", vlm_gpu_memory_utilization=0.0))
-    assert eng2.prelaunch_vram_gb() == pytest.approx(21.6)
+    assert eng2.prelaunch_vram_gb() == pytest.approx((0.9 + _headroom) * 24.0)
 
 
 def test_a_shared_server_is_reserved_for_ONCE_not_per_instance(monkeypatch):
@@ -423,7 +441,7 @@ def test_the_model_manager_can_evict_ocr_including_both_vlm_servers(monkeypatch)
         planned_vram_gb=lambda vcfg, share=None: 8.4,
         ensure_service=lambda *a, **k: "http://127.0.0.1:1/",
     )
-    monkeypatch.setitem(sys.modules, "codai.api.vllm_worker", fake_worker)
+    _patch_vllm_worker(monkeypatch, fake_worker)
     monkeypatch.setattr("codai.models.manager.get_active_vllm_config",
                         lambda: types.SimpleNamespace(gpu_memory_utilization=0.9))