---

name: Vram-GPU-OOM description: GPU VRAM management patterns for sharing memory across services (Ollama, Whisper, ComfyUI). OOM retry logic, auto-unload on idle, and service signaling protocol.

GPU OOM Retry Pattern

Simple pattern for sharing GPU memory across multiple services without coordination.

Strategy

1. All services try to load models normally
2. Catch OOM errors
3. Wait 30-60 seconds (for other services to auto-unload)
4. Retry up to 3 times
5. Configure all services to unload quickly when idle

Python (PyTorch / Transformers)

Copyimport torch
import time

def load_model_with_retry(max_retries=3, retry_delay=30):
    for attempt in range(max_retries):
        try:
            # Your model loading code
            model = MyModel.from_pretrained("model-name")
            model.to("cuda")
            return model

        except RuntimeError as e:
            if "out of memory" in str(e).lower():
                if attempt < max_retries - 1:
                    print(f"OOM on attempt {attempt+1}, waiting {retry_delay}s...")
                    torch.cuda.empty_cache()  # Clean up
                    time.sleep(retry_delay)
                else:
                    raise  # Give up after max retries
            else:
                raise  # Not OOM, raise immediately

ComfyUI / Flux (Python-based)

Add to your workflow/node:

Copy# In your model loading function
import torch
import time

def load_flux_model(path, max_retries=3):
    for attempt in range(max_retries):
        try:
            # Your Flux/ComfyUI loading code
            model = comfy.utils.load_torch_file(path)
            return model
        except RuntimeError as e:
            if "out of memory" in str(e).lower():
                if attempt < max_retries - 1:
                    print(f"GPU busy, retrying in 30s...")
                    torch.cuda.empty_cache()
                    time.sleep(30)
                else:
                    raise
            else:
                raise

Ollama

Ollama already handles this! Just configure quick unloading:

Copy# In /etc/systemd/system/ollama.service.d/override.conf
Environment="OLLAMA_KEEP_ALIVE=30s"

Shell Scripts

For any GPU command:

Copy#!/bin/bash
MAX_RETRIES=3
RETRY_DELAY=30

for i in $(seq 1 $MAX_RETRIES); do
    if your-gpu-command; then
        exit 0
    fi

    if [ $i -lt $MAX_RETRIES ]; then
        echo "GPU busy, retrying in ${RETRY_DELAY}s..."
        sleep $RETRY_DELAY
    fi
done

echo "Failed after $MAX_RETRIES attempts"
exit 1

Service Signaling Protocol (Optional Enhancement)

For better coordination, services can implement these endpoints:

1. Auto-Unload on Idle

Services can automatically unload models after idle timeout:

Copy# FastAPI example
import asyncio
import time

last_request_time = None
auto_unload_minutes = 5  # configurable

async def auto_unload_task():
    """Background task that unloads model after idle timeout."""
    while True:
        await asyncio.sleep(60)  # Check every minute

        if current_handler is None:
            continue

        idle = time.time() - last_request_time
        if idle > (auto_unload_minutes * 60):
            logger.info(f"Auto-unloading model after {idle/60:.1f} minutes")
            current_handler.unload()
            current_handler = None

@app.on_event("startup")
async def startup():
    asyncio.create_task(auto_unload_task())

2. Request-Unload Endpoint

Allow other services to politely request unload:

Copy@app.post("/request-unload")
async def request_unload():
    """Request model unload if idle."""
    if current_handler is None:
        return {"status": "ok", "unloaded": False, "message": "No model loaded"}

    idle = time.time() - last_request_time

    # Only unload if idle for at least 30 seconds
    if idle < 30:
        return {
            "status": "busy",
            "unloaded": False,
            "message": f"Model in use (idle {idle:.0f}s)",
            "idle_seconds": idle,
        }

    # Unload the model
    logger.info("Unloading on request from another service")
    current_handler.unload()
    current_handler = None

    return {
        "status": "ok",
        "unloaded": True,
        "message": "Model unloaded",
        "idle_seconds": idle,
    }

3. Enhanced Status Endpoint

Copy@app.get("/status")
async def get_status():
    idle = time.time() - last_request_time if last_request_time else None
    return {
        "status": "ok",
        "model_loaded": current_handler is not None,
        "idle_seconds": idle,
        "auto_unload_enabled": auto_unload_minutes is not None,
        "auto_unload_minutes": auto_unload_minutes,
    }

4. Using the Protocol

Before loading a large model, request other services to unload:

Copyimport requests

SERVICES = [
    "http://10.99.0.3:8765",  # Invoice OCR
    # Add other services here
]

for service in SERVICES:
    try:
        resp = requests.post(f"{service}/request-unload", timeout=5)
        result = resp.json()
        if result.get("unloaded"):
            print(f"✓ {service} unloaded")
        elif result.get("status") == "busy":
            print(f"⏱ {service} busy, will retry OOM")
    except:
        pass  # Service not available

# Now try to load your model (with OOM retry as backup)

Helper script: See request_gpu_unload.py in OneCuriousRabbit repo.

Key Settings

Invoice OCR (Qwen2-VL)

✅ OOM retry: 3x with 30s delays ✅ Auto-unload: 5 minutes idle (configurable via --auto-unload-minutes) ✅ Request-unload endpoint: POST http://10.99.0.3:8765/request-unload

Ollama

✅ Auto-unload: OLLAMA_KEEP_ALIVE=30s in systemd override

Your Other Services

1. Implement OOM retry pattern (required)
2. Optionally implement signaling protocol (auto-unload + request-unload endpoints)

How It Works

Passive (OOM Retry Only)

12:00 - Scheduled Qwen task starts, loads 4GB 12:01 - User uploads invoice, tries to load 18GB → OOM 12:01 - Invoice OCR waits 30s 12:01:30 - Qwen task finishes, auto-unloads after 30s 12:02 - Invoice OCR retry succeeds, loads 18GB 12:03 - Invoice processing completes, unloads 12:03:30 - GPU is free again

Active (With Signaling)

12:00 - User starts Flux generation 12:00 - Flux calls POST /request-unload on Invoice OCR 12:00 - Invoice OCR idle for 4 minutes → unloads immediately 12:00 - Flux loads its model (22GB) successfully 12:05 - Flux completes, auto-unloads after 5 minutes

Benefits of signaling:

• Faster starts (no waiting for OOM retry delays)
• More predictable behavior
• Can request unload proactively before attempting load
• OOM retry still works as fallback if service is busy