Why My Ollama + LangChain FastAPI service on Ubuntu 22.04 keeps crashing with “CUDA out of memory” after the latest vLLM 0.3.0 upgrade – step‑by‑step fix for GPU+Docker misconfiguration.

Required Tools and Environment Setup

• NVIDIA GPU with at least 16GB VRAM (tested on RTX 3090, A100, H100)
• Ubuntu 22.04 LTS or similar Debian-based distribution
• NVIDIA CUDA 12.1+ and cuDNN installed on host
• Docker 24.0+ with NVIDIA Container Toolkit
• Ollama 0.1.0+ running as a service on the host or in a separate container
• vLLM 0.3.0+ (the problematic version that prompted this fix)
• LangChain 0.1.0+ with vLLM integration
• FastAPI 0.104+ and Uvicorn
• Docker Compose 2.0+ (recommended for orchestration)
• nvidia-smi command-line utility (comes with CUDA)
• A text editor or IDE to modify configuration files

Understanding the Root Cause: Why vLLM 0.3.0 Changed Everything

When vLLM upgraded to version 0.3.0, it introduced significant changes to how it manages CUDA memory allocation and GPU device visibility. The new version is more aggressive about pre-allocating GPU memory and has stricter requirements for how environment variables are passed through Docker containers.

The typical scenario: your Docker container can’t properly detect GPU availability, vLLM falls back to CPU inference (which is catastrophically slow), LangChain queues up requests, and suddenly your model tries to load into CPU RAM or spills over into a non-existent GPU, causing an out-of-memory crash within seconds.

The real problem isn’t your GPU capacity — it’s that the GPU isn’t being exposed to your Docker container correctly, or memory allocation flags have changed in vLLM 0.3.0, or Ollama and vLLM are competing for the same GPU without proper isolation.

Step-by-Step Diagnostic and Fix Workflow

1Verify GPU Availability on Your Host

Before touching Docker, confirm your GPU is healthy and accessible:

nvidia-smi

You should see output showing your GPU(s), memory usage, and running processes. If this command fails, your CUDA installation is broken — fix that before proceeding.

Check GPU memory in detail:

nvidia-smi --query-gpu=index,name,memory.total,memory.used,memory.free --format=csv,noheader

Record the total and free memory. This is your baseline.

2Verify NVIDIA Container Toolkit is Installed and Running

This is critical. Docker doesn’t automatically pass GPU access to containers — the NVIDIA Container Toolkit acts as a bridge.

docker run --rm --gpus all ubuntu nvidia-smi

If this fails with “docker: Error response from daemon,” you need to install the toolkit:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
  && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

After installation, test again. You should see GPU info inside the container.

3Stop Competing Ollama and vLLM Processes

If Ollama is running on your host (not in Docker), it’s likely monopolizing your GPU. Check:

ps aux | grep ollama
ps aux | grep vllm

Kill any running instances:

sudo systemctl stop ollama
# or
killall ollama
killall vllm

Verify the GPU is now free:

nvidia-smi

You should see “No processes” in the GPU memory section.

4Create a Proper docker-compose.yml with GPU Configuration

This is where most configurations fail. Here’s a battle-tested example:

version: '3.8'

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama-service
    environment:
      - OLLAMA_HOST=0.0.0.0:11434
      - CUDA_VISIBLE_DEVICES=0
    volumes:
      - ollama_data:/root/.ollama
    ports:
      - "11434:11434"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

  fastapi_app:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: fastapi-llm-service
    environment:
      - OLLAMA_HOST=http://ollama-service:11434
      - CUDA_VISIBLE_DEVICES=0
      - VLLM_GPU_MEMORY_UTILIZATION=0.85
      - VLLM_ENFORCE_EAGER=False
      - PYTHONUNBUFFERED=1
    depends_on:
      ollama:
        condition: service_healthy
    ports:
      - "8000:8000"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped
    volumes:
      - ./app:/app
    working_dir: /app
    command: uvicorn main:app --host 0.0.0.0 --port 8000 --reload

volumes:
  ollama_data:

5Update Your Dockerfile to Handle vLLM 0.3.0 Properly

Your Dockerfile must use the official NVIDIA CUDA base image and set critical environment variables at build time:

FROM nvidia/cuda:12.1.1-runtime-ubuntu22.04

# Install system dependencies
RUN apt-get update && apt-get install -y \
    python3.10 \
    python3-pip \
    curl \
    git \
    && rm -rf /var/lib/apt/lists/*

# Set Python environment
ENV PYTHONUNBUFFERED=1
ENV CUDA_HOME=/usr/local/cuda
ENV PATH=${CUDA_HOME}/bin:${PATH}
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}

WORKDIR /app

# Copy requirements
COPY requirements.txt .

# Install Python dependencies (vLLM must be installed with CUDA support)
RUN pip3 install --no-cache-dir \
    vllm==0.3.0 \
    ollama==0.0.18 \
    langchain==0.1.0 \
    fastapi==0.104.1 \
    uvicorn==0.24.0 \
    pydantic==2.0.0 \
    torch==2.1.0 \
    transformers==4.34.0

# Copy application code
COPY . .

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
    CMD curl -f http://localhost:8000/health || exit 1

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

6Configure Your FastAPI Application with Proper vLLM Initialization

This is where LangChain and vLLM actually connect. The initialization matters enormously:

from fastapi import FastAPI, HTTPException
from langchain.llms.vllm import VLLM
from langchain.callbacks.manager import CallbackManager
import os
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="LLM Service", version="1.0.0")

# Initialize vLLM with explicit GPU settings
llm = None

@app.on_event("startup")
async def startup_event():
    global llm
    try:
        logger.info("Initializing vLLM with LangChain...")
        
        llm = VLLM(
            model="mistral",  # Adjust to your model
            trust_remote_code=True,
            max_new_tokens=512,
            temperature=0.7,
            tensor_parallel_size=1,
            gpu_memory_utilization=0.85,
            enforce_eager=False,
            dtype="auto",
        )
        logger.info("✓ vLLM initialized successfully")
        
    except Exception as e:
        logger.error(f"✗ Failed to initialize vLLM: {str(e)}")
        raise

@app.on_event("shutdown")
async def shutdown_event():
    global llm
    if llm:
        logger.info("Shutting down vLLM...")
        # vLLM handles cleanup automatically

@app.get("/health")
async def health_check():
    return {"status": "healthy", "gpu_available": True}

@app.post("/generate")
async def generate(prompt: str, max_tokens: int = 256):
    if llm is None:
        raise HTTPException(status_code=503, detail="LLM not initialized")
    
    try:
        response = llm(prompt, max_tokens=max_tokens)
        return {"response": response}
    except RuntimeError as e:
        if "CUDA" in str(e) and "out of memory" in str(e):
            logger.error(f"CUDA OOM detected: {str(e)}")
            raise HTTPException(status_code=507, detail="GPU memory exhausted")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/batch-generate")
async def batch_generate(prompts: list[str], max_tokens: int = 256):
    if llm is None:
        raise HTTPException(status_code=503, detail="LLM not initialized")
    
    results = []
    for prompt in prompts:
        try:
            response = llm(prompt, max_tokens=max_tokens)
            results.append({"prompt": prompt, "response": response})
        except RuntimeError as e:
            logger.warning(f"Batch item failed: {str(e)}")
            results.append({"prompt": prompt, "error": str(e)})
    
    return {"results": results}

Key configuration parameters that changed in vLLM 0.3.0:

• gpu_memory_utilization=0.85 — Start conservative; lower values = less OOM risk, but also lower throughput
• enforce_eager=False — Allows graph execution (more memory-efficient in 0.3.0)
• dtype="auto" — Automatically selects the best data type for your GPU
• tensor_parallel_size=1 — No multi-GPU sharding (set to number of GPUs if using multiple)

7Set Environment Variables in Your .env File

Create a .env file in your project root:

# GPU Configuration
CUDA_VISIBLE_DEVICES=0
CUDA_DEVICE_ORDER=PCI_BUS_ID

# vLLM Configuration (critical for 0.3.0)
VLLM_GPU_MEMORY_UTILIZATION=0.85
VLLM_ENFORCE_EAGER=False
VLLM_TENSOR_PARALLEL_SIZE=1

# Ollama Configuration
OLLAMA_HOST=http://ollama-service:11434
OLLAMA_NUM_GPU=1

# FastAPI Configuration
PYTHONUNBUFFERED=1
LOG_LEVEL=INFO

Load these in your docker-compose.yml:

env_file:
  - .env

8Launch and Monitor

Now bring everything up:

docker-compose down  # Clean slate
docker-compose up --build

In another terminal, monitor GPU usage in real-time:

watch -n 1 nvidia-smi

Test your API:

curl -X POST http://localhost:8000/generate \
  -H "Content-Type: application/json" \
  -d '{"prompt": "What is the capital of France?", "max_tokens": 50}'

Check the logs:

docker-compose logs -f fastapi_app

If you see “CUDA out of memory,” move to the troubleshooting section below.

Common Mistakes and Why They Happen

Mistake #1: Forgetting the NVIDIA Container Toolkit

What happens: Docker can’t see your GPU, vLLM defaults to CPU, and everything crawls to a halt or crashes.

Why it’s easy to miss: Docker itself runs fine without it. The toolkit is a separate layer that most developers don’t know about until they hit this wall.

Fix: Install the toolkit (Step 2 above) and verify with docker run --rm --gpus all ubuntu nvidia-smi.

Mistake #2: Not Explicitly Assigning GPU Devices in docker-compose.yml

What happens: Ollama and vLLM both try to use GPU 0, competing for memory. Whichever starts second crashes.

Why it’s easy to miss: Docker doesn’t automatically isolate GPU usage by device. You have to be explicit.

Fix: Use CUDA_VISIBLE_DEVICES=X per service and ensure each service has its own GPU or explicitly shares one.

Mistake #3: Using a Non-NVIDIA Base Image in Dockerfile

What happens: CUDA libraries are missing or misconfigured inside the container. vLLM can’t access GPU memory properly.

Why it’s easy to miss: A generic Ubuntu image works for CPU workloads, so it seems fine until you try to use GPU features.

Fix: Always use nvidia/cuda:12.1.1-runtime-ubuntu22.04 (or newer) as your base image.

Mistake #4: Not Setting CUDA Library Paths

What happens: Container starts but can’t find CUDA libraries at runtime. vLLM initialization fails with cryptic errors.

Why it’s easy to miss: Works locally because your host has CUDA in the system PATH. Containers don’t inherit that.

Fix: Set ENV CUDA_HOME, ENV PATH, and ENV LD_LIBRARY_PATH in your Dockerfile (see Step 5).

Mistake #5: Running Ollama and vLLM on the Same GPU Without Resource Limits

What happens: Both services load their models into GPU memory simultaneously. Instant OOM.

Why it’s easy to miss: Works in dev when you’re only running one. Production traffic triggers both services to wake up.

Fix: Isolate services to different GPUs or use CPU for one of them. If sharing one GPU, implement request queuing and sequential processing.

Mistake #6: Ignoring vLLM 0.3.0 Memory Configuration Changes

What happens: vLLM 0.3.0 pre-allocates GPU memory differently than 0.2.x. Your old config allocates too much, causing OOM even with headroom.

Why it’s easy to miss: You upgraded vLLM but didn’t read the changelog. Oops.

Fix: Explicitly set gpu_memory_utilization=0.85 and test with lower values (0.7, 0.75) initially.

Optimization Tips and Follow-Up Checks

Optimize gpu_memory_utilization Gradually

Start conservative and increase incrementally:

Test each level under realistic load for at least 10 minutes:

docker-compose exec fastapi_app curl -X POST http://localhost:8000/batch-generate \
  -H "Content-Type: application/json" \
  -d '{"prompts": ["test"] * 100, "max_tokens": 256}'

Monitor GPU memory usage throughout:

watch -n 0.5 nvidia-smi dmon

Enable Detailed Logging in vLLM

Add this to your FastAPI startup to catch memory issues early:

import logging
logging.getLogger("vllm").setLevel(logging.DEBUG)
logging.getLogger("vllm.engine").setLevel(logging.DEBUG)

Implement Memory Monitoring Endpoints

Add a diagnostic endpoint to your FastAPI app:

@app.get("/metrics/gpu")
async def gpu_metrics():
    import subprocess
    result = subprocess.run(
        ["nvidia-smi", "--query-gpu=index,name,memory.used,memory.total,utilization.gpu", 
         "--format=csv,nounits,noheader"],
        capture_output=True,
        text=True
    )
    lines = result.stdout.strip().split('\n')
    metrics = []
    for line in lines:
        parts = line.split(', ')
        metrics.append({
            "gpu_id": parts[0],
            "gpu_name": parts[1],
            "memory_used_mb": int(parts[2]),
            "memory_total_mb": int(parts[3]),
            "utilization_percent": int(parts[4]),
        })
    return {"gpus": metrics}

Set Resource Limits in docker-compose.yml

Prevent one container from starving others:

services:
  fastapi_app:
    deploy:
      resources:
        limits:
          memory: 16G  # CPU memory cap
        reservations:
          memory: 8G   # Minimum guaranteed
          devices:
            - driver: nvidia
              device_ids: ['0']  # Explicitly use GPU 0
              capabilities: [gpu]

Implement Graceful Degradation

If OOM happens, degrade gracefully instead of crashing:

from functools import lru_cache

RETRY_BACKOFF = 2  # seconds
MAX_RETRIES = 3

@app.post("/generate")
async def generate(prompt: str, max_tokens: int = 256):
    for attempt in range(MAX_RETRIES):
        try:
            response = llm(prompt, max_tokens=max_tokens)
            return {"response": response}
        except RuntimeError as e:
            if "CUDA" in str(e) and "out of memory" in str(e):
                if attempt < MAX_RETRIES - 1:
                    logger.warning(f"OOM on attempt {attempt+1}, retrying...")
                    await asyncio.sleep(RETRY_BACKOFF * (attempt + 1))
                    continue
                else:
                    raise HTTPException(
                        status_code=507,
                        detail="GPU memory exhausted after retries"
                    )
            raise

Real-World Example: Before and After

Before: The Crash Scenario

Setup: RTX 3090 (24GB), Ubuntu 22.04, vLLM 0.3.0, Mistral-7B, FastAPI with LangChain.

docker-compose.yml:

version: '3.8'
services:
  app:
    build: .
    ports:
      - "8000:8000"
    # ❌ NO GPU SPECIFICATION

Dockerfile:

<code">FROM ubuntu:22.04  # ❌ No CUDA libraries
# ... missing CUDA env vars

What happened:

$ curl -X POST http://localhost:8000/generate -d '{"prompt": "Hello", "max_tokens": 100}'
RuntimeError: CUDA out of memory. Tried to allocate 3.50 GiB
(venv) $ docker-compose logs fastapi_app | tail -20
fastapi_app_1  | Traceback (most recent call last):
fastapi_app_1  |   File "/app/main.py", line 15, in startup_event
fastapi_app_1  |     llm = VLLM(model="mistral", ...)
fastapi_app_1  | RuntimeError: Could not initialize model. CUDA not available.

After: The Fixed Configuration

Same setup, fixed config.

docker-compose.yml:

version: '3.8'
services:
  app:
    build: .
    environment:
      - CUDA_VISIBLE_DEVICES=0
      - VLLM_GPU_MEMORY_UTILIZATION=0.85
    ports:
      - "8000:8000"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Dockerfile:

FROM nvidia/cuda:12.1.1-runtime-ubuntu22.04
ENV CUDA_HOME=/usr/local/cuda
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
# ... proper dependencies

What happened:

$ docker-compose up --build
...
fastapi_app_1  | INFO: Uvicorn running on http://0.0.0.0:8000
fastapi_app_1  | ✓ vLLM initialized successfully

$ curl -X POST http://localhost:8000/generate -d '{"prompt": "Hello", "max_tokens": 100}'
{"response": "Hello! I'm Mistral, an AI assistant. How can I help you today?"}

$ nvidia-smi
+-----------------------------------------------------------------------------+
| GPU  Name           Persistence-M| Bus-Id        Disp.A | Memory-Usage | Temp |
|=============================================================================|
|   0  NVIDIA RTX 3090  On            | 00:1F.0     Off |      7234MB / 24576MB |  48C |
+-----------------------------------------------------------------------------+

Success. No crashes. GPU memory is being used efficiently. The service stays up under load.

Comprehensive Troubleshooting Checklist

1. GPU availability: Run nvidia-smi on the host. If it fails, fix CUDA installation first.
2. Container GPU access: Run docker run --rm --gpus all ubuntu nvidia-smi. If it fails, reinstall NVIDIA Container Toolkit.
3. Base image: Verify your Dockerfile starts with FROM nvidia/cuda:12.1.1-runtime-ubuntu22.04.
4. Environment variables: Confirm CUDA_VISIBLE_DEVICES, CUDA_HOME, LD_LIBRARY_PATH are set in Dockerfile.
5. No competing processes: Kill any host-level Ollama or vLLM: killall ollama vllm.
6. Memory allocation: Set gpu_memory_utilization=0.7 initially, then increase.
7. vLLM version: Confirm you’re on 0.3.0+: pip show vllm.
8. Model size: Ensure your model fits in GPU memory (Mistral-7B needs ~16GB with some overhead).
9. Logs are verbose: Check docker-compose logs -f fastapi_app for actual error messages.
10. Docker memory isn’t bottlenecking CPU RAM: Check free -h and docker stats.

When to Use vLLM vs. Ollama Alone

This guide combines Ollama and vLLM. Here’s when that makes sense:

For this guide’s use case (production FastAPI service with LangChain), vLLM + Ollama gives you the best of both worlds: Ollama’s model management and vLLM’s performance.

Performance Benchmarks After Fixes

Once your configuration is correct, here’s what you should expect on an RTX 3090 with Mistral-7B:

Final Checklist Before Production

• Tested under 2x expected peak load for 30+ minutes without crashes
• GPU memory utilization stays under 95% at peak
• Response times are consistent (no sudden slowdowns)
• Health check endpoint works: curl http://localhost:8000/health
• Logs are clean (no CUDA warnings or memory fragmentation alerts)
• Container restarts cleanly: docker-compose restart works without hanging
• Monitoring in place (Prometheus, Datadog, or equivalent tracking GPU metrics)
• Alert configured for CUDA OOM errors
• Documented rollback plan if vLLM 0.4.0 breaks this config again
• Team understands the setup and troubleshooting steps

Next Steps: Advanced Optimization

Once your service is stable, consider these improvements:

• Model quantization: Use 4-bit or 8-bit quantization to reduce memory (AWQ, GPTQ formats)
• Speculative decoding: vLLM 0.3.0+ supports this for 2–3x speedup on certain workloads
• Prefix caching: Reuse computed tokens for repeated prompts (huge win for RAG systems)
• Multi-GPU serving: Shard models across GPUs for larger models (Llama-70B, etc.)
• LoRA fine-tuning: Serve custom-tuned models alongside base model
• Kubernetes deployment: Replace Docker Compose with K8s for true production scaling

Conclusion: Why This Happens and How to Prevent It

Additional Resources

• vLLM Official Documentation: https://docs.vllm.ai — Always check here for version-specific changes
• NVIDIA Container Toolkit Setup: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
• Ollama Documentation: https://github.com/ollama/ollama — Model management and deployment
• LangChain vLLM Integration: https://python.langchain.com/docs/integrations/llms/vllm — Framework specifics
• Docker GPU Support: https://docs.docker.com/config/containers/resource_constraints/#gpu — Device visibility and limits
• Ubuntu 22.04 CUDA Installation: https://developer.nvidia.com/cuda-downloads — Official NVIDIA guide

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