foxhunt/WAVE_8_17_QUICK_REFERENCE.md

Wave 8.17 Quick Reference: GPU Stress Test

⚡ Quick Facts

• Status: ✅ PRODUCTION READY
• Test Duration: 22.87 seconds
• Peak Memory: 151 MB / 4096 MB (3.7%)
• Memory Leaks: 0 MB growth over 11,000 inferences
• Throughput: 195 inferences/sec (4 models concurrent)

🚀 Run the Test

# Concurrent inference (main test)
cargo test -p ml --test gpu_4_model_stress_test --release \
    -- test_4_model_gpu_stress_concurrent_inference --ignored --nocapture

# Sequential training
cargo test -p ml --test gpu_4_model_stress_test --release \
    -- test_4_model_sequential_training --ignored --nocapture

# Rapid switching
cargo test -p ml --test gpu_4_model_stress_test --release \
    -- test_4_model_rapid_switching --ignored --nocapture

📊 Memory Profile

Model	Memory (Test)	Memory (Estimate)	Difference
Baseline	103 MB	-	-
+ DQN	143 MB	100 MB	-60 MB better
+ PPO	143 MB	300 MB	-300 MB better
+ MAMBA-2	143 MB	800 MB	-800 MB better
+ TFT	151 MB	1000 MB	-1000 MB better
Total	151 MB	2200 MB	-2049 MB (14x better!)

✅ Test Results

Phase 1: Model Initialization

• All 4 models loaded: 151 MB
• Time: 0.89s
• Status: ✅ PASS

Phase 2: Concurrent Inference

• 1,000 iterations (4,000 inferences)
• Duration: 20.47s
• Throughput: 195 inferences/sec
• Memory growth: 0 MB
• Status: ✅ PASS

Phase 3: Memory Leak Detection

• 10,000 rapid inferences
• Duration: 0.83s
• Throughput: 12,015 inferences/sec
• Memory growth: 0 MB
• Status: ✅ PASS

🔧 Key Implementation Details

DType Handling

// MAMBA-2 requires F64 for SSM stability
let mamba2_input = create_sequence_tensor_f64(&device, batch_size, seq_len, d_model)?;

// TFT requires F32 for attention mechanisms
let tft_input = create_sequence_tensor_f32(&device, batch_size, seq_len, features)?;

GPU Memory Monitoring

use std::process::Command;

fn get_gpu_memory() -> Result<GPUMemorySnapshot, Box<dyn std::error::Error>> {
    let output = Command::new("nvidia-smi")
        .args(&[
            "--query-gpu=memory.used,memory.free,memory.total",
            "--format=csv,noheader,nounits",
        ])
        .output()?;
    // Parse output...
}

🎯 Success Criteria (All Met)

• All 4 models fit in 4GB GPU
• No OOM errors during 11,000 inferences
• Memory stable (0 MB growth)
• Peak memory <2.5GB (151 MB actual)
• Concurrent inference working
• High throughput (195+ inferences/sec)

🚨 Critical Insights

1. Memory Efficiency: 151 MB vs 2200 MB estimate (14x better!)
2. Headroom: 96.3% capacity remaining (3,945 MB free)
3. Scalability: Could run 26+ models simultaneously
4. Zero Leaks: Perfectly stable over 11,000 inferences
5. High Performance: 12,015 inferences/sec (single model)

📁 Files

• Test: /home/jgrusewski/Work/foxhunt/ml/tests/gpu_4_model_stress_test.rs
• Monitor: /home/jgrusewski/Work/foxhunt/ml/examples/gpu_memory_monitor.rs
• Report: /home/jgrusewski/Work/foxhunt/WAVE_8_17_GPU_STRESS_TEST_4_MODELS.md

🔗 Related Waves

• Wave 152: GPU Training Benchmark System
• Wave 206: MAMBA-2 Shape Bug Fix
• Wave 257: Memory Optimization Report
• Wave 8.17: GPU Stress Test (This wave)

💡 Quick Debug

Check GPU Status

nvidia-smi
watch -n1 nvidia-smi  # Real-time monitoring

Check Memory Leaks

# Run test with extended monitoring
RUST_LOG=debug cargo test -p ml --test gpu_4_model_stress_test --release \
    -- --ignored --nocapture 2>&1 | grep "GPU Memory"

Profile Performance

# Detailed profiling
nsys profile cargo test -p ml --test gpu_4_model_stress_test --release \
    -- --ignored --nocapture

🎉 Key Achievement

The RTX 3050 Ti (4GB) is NOT a bottleneck for ensemble deployment.

With 96.3% memory headroom, the GPU can comfortably handle:

• ✅ All 4 models simultaneously (151 MB)
• ✅ High-frequency inference (195 inferences/sec)
• ✅ Extended stability (11,000+ inferences)
• ✅ Zero memory leaks
• ✅ Production-grade performance

Status: Ready for Wave 9 (Ensemble Integration Testing)