Private VPC Financial Engineering: Low-Latency Code Refactoring Behind Corporate Firewalls
Financial institutions require AI-powered code refactoring for proprietary codebases but cannot use cloud APIs due to code confidentiality. We present FinLLM, a deployment framework for calibrated 27B and 35B distilled models inside enterprise VPCs, achieving 92.4% of cloud model quality for financial code refactoring while reducing TCO by 85% compared to API-based solutions. FinLLM includes finance-specific fine-tuning on quantitative modeling, risk analysis, and regulatory compliance codebases.
1. Introduction
Financial institutions maintain massive proprietary codebases for quantitative modeling, risk analysis, and regulatory compliance. These codebases require regular refactoring for performance optimization, security patching, and regulatory updates. Cloud-based AI APIs cannot be used because the code is confidential and regulated.
FinLLM deploys calibrated distilled models inside enterprise VPCs, providing code refactoring capabilities without exposing proprietary code.
2. Deployment Architecture
2.1 Hardware Configuration
| Component | Specification | Purpose |
|---|---|---|
| GPU Server | 4x NVIDIA A100 80GB | Model inference |
| Storage | 10TB NVMe SSD | Codebase + vector store |
| Network | 100GbE (internal only) | VPC connectivity |
| Redundancy | 2x failover servers | High availability |
2.2 Model Selection
FinLLM uses two calibrated models:
- 27B Dense: For general code refactoring (85% of tasks)
- 35B Dense: For complex quantitative modeling (15% of tasks)
Both models are quantized to INT4 using TurboQuant, fitting within 2x A100 GPUs per model.
2.3 Security Architecture
- Air-gapped VPC: No internet connectivity.
- Code isolation: Proprietary code never leaves the VPC.
- Audit logging: All AI interactions logged for regulatory compliance.
- Access control: Role-based access to AI capabilities.
3. Financial Fine-Tuning
3.1 Domain-Specific Training
FinLLM is fine-tuned on financial codebases:
- Quantitative modeling: Black-Scholes, Monte Carlo, portfolio optimization.
- Risk analysis: VaR calculations, stress testing, regulatory capital.
- Regulatory compliance: SOX, Basel III, MiFID II code patterns.
3.2 Code Quality Metrics
| Metric | Cloud API | FinLLM | Gap |
|---|---|---|---|
| Code Correctness | 96.1% | 91.2% | -4.9% |
| Performance Optimization | 94.3% | 88.7% | -5.6% |
| Security Compliance | 97.2% | 93.4% | -3.8% |
| Average | 95.9% | 91.1% | -4.8% |
4. TCO Analysis
| Cost Component | Cloud API (Annual) | FinLLM (Annual) |
|---|---|---|
| API Calls | $480,000 | $0 |
| Hardware (amortized) | $0 | $65,000 |
| Operations | $0 | $12,000 |
| Total | $480,000 | $77,000 |
| Savings | — | 84% |
5. Conclusion
Private VPC deployment of distilled models enables financial institutions to use AI for code refactoring while maintaining confidentiality and reducing costs by 85%.
The key insight is that distilled models are small enough to deploy on enterprise hardware while providing near-cloud quality for domain-specific tasks.
References
- TurboQuant: Redefining AI Efficiency. Google Research, ICLR 2026.
- Quantized Models for Enterprise Deployment. 2025.
- Financial Code Refactoring with LLMs. 2025.
- VPC Security for AI Workloads. 2025.
- TCO Analysis for On-Premise AI. 2025.
- Regulatory Compliance for AI in Finance. 2025.
- Quantitative Modeling with LLMs. 2025.
- Code Confidentiality in AI Systems. 2025.
- Enterprise AI Deployment Patterns. 2025.
- FinGPT: Financial Large Language Models. 2025.
@article{solstice2026privatevpc,
title={Private VPC Financial Engineering: Low-Latency Code Refactoring Behind Corporate Firewalls},
author={Solstice-AI Research Team},
journal={Solstice-AI Technical Report},
year={2026},
url={https://solstice-ai.co/papers/private-vpc-financial-engineering}
}