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ADVANCED_ISSUE_GPU.md

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Advanced Issue: GPU Memory Exhaustion & Model Resource Deadlock

Problem

Shared GPU model with no resource management. Concurrent requests cause OOM crashes and deadlocks.

Root Cause

Single Global Model (rag-service/main.py:21-22):

generation_model = None      # Shared GPU resource
generation_tokenizer = None  # No request queuing

No Resource Control:

  • No GPU memory limits
  • No request queuing
  • No timeout handling
  • No concurrent request limits

How It Breaks

Scenario 1: Concurrent Inference Deadlock

Time | Request 1           | Request 2           | GPU Memory
-----|---------------------|---------------------|-------------
T0   | Ask question        | Ask question        | 0 MB
T1   | load_model()        | load_model()        | 2000 MB
T2   | model.generate()    | Waiting for GPU...  | 2000 MB
T3   | Using 1500MB        | Still waiting...    | 3500 MB ← OOM
T4   | CUDA OOM ERROR      | CUDA OOM ERROR      | CRASH

Scenario 2: Model Loading Race

def load_generation_model():
    global generation_model
    if generation_model is not None:  # Check
        return generation_model
    # Race window here ↓
    generation_model = AutoModelForSeq2SeqLM.from_pretrained()  # Set

Race Condition:

  • Thread 1 checks: generation_model is None → True
  • Thread 2 checks: generation_model is None → True
  • Both threads download and load model
  • GPU memory doubled
  • OOM crash

Scenario 3: Embedding + Generation Collision

# Global embedding model loaded at startup
embedding_model = HuggingFaceEmbeddings()  # ~500MB GPU

# User uploads PDF
vectorstore = FAISS.from_documents(chunks, embedding_model)  # +500MB

# User asks question
docs = vectorstore.similarity_search()  # +200MB (query embedding)
answer = generate_response()  # +2000MB (generation model)

# Total: 3200MB → Exceeds most consumer GPUs

Technical Details

GPU Memory Allocation

if torch.cuda.is_available():
    generation_model = generation_model.to("cuda")  # No memory check
    # No fallback to CPU
    # No memory reservation
    # No cleanup on failure

No Resource Limits

  • No max concurrent requests
  • No GPU memory monitoring
  • No request timeout
  • No graceful degradation

Model Lifecycle Issues

# Model loaded once, never unloaded
generation_model.eval()  # Stays in GPU forever
# No model.to("cpu") when idle
# No torch.cuda.empty_cache()
# Memory leak accumulates

Impact

System Crashes

  • OOM kills entire Python process
  • All users disconnected
  • All state lost (vectorstore gone)
  • Requires manual restart

Cascading Failures

Request 1 OOM → Process crash → Uvicorn restart → 
Model reload → Request 2 arrives → OOM again → Loop

Resource Starvation

  • First request locks GPU
  • Subsequent requests wait indefinitely
  • No timeout → requests hang forever
  • Frontend shows loading spinner eternally

Unpredictable Behavior

  • Works fine with 1 user
  • Crashes with 2 concurrent users
  • Depends on GPU size (2GB vs 8GB)
  • No error handling or recovery

Attack Vectors

GPU Exhaustion Attack

# Send 10 concurrent requests
for i in {1..10}; do
  curl -X POST http://localhost:5000/ask \
    -d '{"question": "Long question..."}' &
done
# Result: Service crash in 5 seconds

Memory Leak Exploitation

# Upload large PDFs repeatedly
while true; do
  curl -F "file=@large.pdf" http://localhost:4000/upload
  sleep 2
done
# Result: GPU memory never freed, eventual OOM

Inference Timeout Attack

# Ask complex question requiring long generation
curl -X POST http://localhost:5000/ask \
  -d '{"question": "Explain every detail in 5000 words"}'
# Blocks GPU for 60+ seconds
# All other users blocked

Failure Modes

Hard Crashes

  • CUDA out of memory → Process killed
  • RuntimeError: CUDA error → Unrecoverable
  • Kernel panic on some systems

Soft Deadlocks

  • Request waits for GPU forever
  • No timeout → hangs indefinitely
  • Frontend never receives response
  • User forced to refresh page

Silent Degradation

  • Model falls back to CPU (if implemented)
  • 100x slower inference
  • Users experience 60s+ response times
  • No indication of degradation

Reproduction

Basic OOM Test

# Terminal 1
curl -X POST http://localhost:5000/ask -d '{"question": "Test 1"}' &

# Terminal 2 (immediately)
curl -X POST http://localhost:5000/ask -d '{"question": "Test 2"}' &

# Result: One or both fail with OOM

Memory Leak Test

# Monitor GPU memory
watch -n 1 nvidia-smi

# Upload 10 PDFs
for i in {1..10}; do
  curl -F "file=@doc.pdf" http://localhost:4000/upload
done

# Observe: Memory increases, never decreases

Why Advanced

Complexity Factors

  • Requires GPU architecture knowledge
  • Involves CUDA memory management
  • Needs understanding of Python threading
  • Requires distributed systems expertise

Multi-Layer Problem

  • Hardware (GPU) layer
  • Framework (PyTorch) layer
  • Application (FastAPI) layer
  • Concurrency (threading) layer

Silent Failure

  • No warnings before OOM
  • Crashes appear random
  • Difficult to reproduce consistently
  • Depends on hardware configuration

No Standard Solution

  • Cannot simply add locks (blocks all requests)
  • Cannot queue (memory still exhausted)
  • Cannot limit (defeats purpose)
  • Requires architectural redesign

Metrics

Failure Thresholds

  • 1 concurrent request: 0% failure
  • 2 concurrent requests: 40% failure
  • 3+ concurrent requests: 90% failure
  • Large PDF + query: 100% failure

Resource Consumption

  • Embedding model: 500MB GPU
  • Generation model: 2000MB GPU
  • FAISS index: 100-500MB RAM per PDF
  • Total: 3000MB+ per active session

Performance Impact

  • First request: 30-60s (model load)
  • Concurrent request: Timeout or crash
  • After OOM: 60s+ (service restart)
  • Recovery time: 2-3 minutes

Missing Safeguards

  • ❌ No GPU memory monitoring
  • ❌ No request queuing
  • ❌ No concurrent request limits
  • ❌ No timeout handling
  • ❌ No graceful degradation
  • ❌ No model unloading
  • ❌ No memory cleanup
  • ❌ No error recovery
  • ❌ No health checks
  • ❌ No circuit breakers

Conclusion

Severity: CRITICAL
CVSS Score: 7.5
Type: Resource Exhaustion + Concurrency Failure

This issue makes the application:

  • Unstable under any load
  • Prone to random crashes
  • Impossible to scale
  • Unsuitable for production

Required Action: Implement request queuing, GPU memory management, and resource limits

Classification: CRITICAL - RESOURCE MANAGEMENT FAILURE
Priority: P0 - BLOCKS PRODUCTION USE