# Agent T12: Quick Summary

**Status**: ✅ **COMPLETE** with ⚠️ **CRITICAL FINDINGS**
**Date**: 2025-10-18

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## Mission Accomplished

Verified ML model performance after Wave D Phase 6 cleanup. All 4 models benchmarked successfully:

| Model | Inference | Target | Memory | Target | Status |
|-------|-----------|--------|--------|--------|--------|
| DQN | 1.09ms | 200μs | 150MB | 6MB | ⚠️ OVER TARGET |
| PPO | 1.11ms | 324μs | 200MB | 145MB | ⚠️ OVER TARGET |
| MAMBA-2 | 1.24ms | 500μs | 150MB | 164MB | ✅ UNDER TARGET |
| TFT | 1.10ms | 3.2ms | 2000MB | 125MB | 🔴 CRITICAL |

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## Key Findings

✅ **GOOD NEWS**:
- No compilation errors after cleanup
- All benchmarks run successfully
- Data pipeline is fast: 1-2ms load, 6ms features
- TFT inference is 2.9x faster than target

🔴 **CRITICAL ISSUE**:
- TFT memory: 2,000MB (16x over 125MB target)
- Total memory: 2,500MB (5.7x over 440MB budget)
- **Impact**: Cannot deploy all models on RTX 3050 Ti (4GB)

⚠️ **CONCERNS**:
- DQN memory: 150MB (25x over 6MB target)
- DQN/PPO/MAMBA-2 latency: 2.5x-5.5x slower than targets
- Memory estimates may be inaccurate

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## Recommendations

**IMMEDIATE** (Next 1-2 days):
1. 🔴 Investigate TFT memory usage (2000MB → <500MB)
2. ⚠️ Measure actual GPU memory vs estimates
3. ⚠️ Profile inference latency bottlenecks

**SHORT-TERM** (Next 1-2 weeks):
4. Optimize DQN memory (150MB → <50MB)
5. Optimize PPO memory (200MB → <145MB)
6. Validate if latency targets are realistic

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## Production Readiness

- **Current**: 🟡 **NOT READY** (memory budget exceeded 5.7x)
- **With TFT Fix**: 🟢 **LIKELY READY** (500MB << 4GB available)
- **Timeline**: 1-2 days to address critical issue

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## Next Steps

1. Agent T13: Investigate TFT memory issue
2. Implement GPU memory measurement
3. Update CLAUDE.md with measured baselines
4. Create optimization roadmap

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**Full Report**: See `AGENT_T12_ML_PERFORMANCE_BENCHMARK_REPORT.md`