Unlocking Transparent AI with GPU-Enabled TEEs and ROFL

In an era where Artificial Intelligence (AI) is becoming a cornerstone of modern industries, a critical question arises: How can we ensure trust in AI models? How do we verify that an AI model was built transparently, trained with the right data, and respects user privacy?

This article explores how GPU-Enabled Trusted Execution Environments (TEEs) and Oasis Runtime Offchain Logic (ROFL) can create AI models with verifiable provenance while publishing this information onchain. These innovations not only enhance transparency and privacy but also pave the way for decentralized AI marketplaces, where trust and collaboration thrive.

What Are GPU-Enabled TEEs and Why Are They Essential in AI?

Trusted Execution Environment (TEE)

A TEE is a secure enclave within hardware that provides a safe environment for sensitive data and application execution. It ensures the integrity of processes, even in cases where the operating system or firmware is compromised.

GPU-Enabled TEEs

GPU-Enabled TEEs are an upgraded version of TEEs, leveraging the computational power of GPUs to handle complex machine learning (ML) tasks securely. A prime example includes:

• NVIDIA H100 GPUs: Capable of integrating with Confidential Virtual Machines (Confidential VMs) to perform secure AI training and inference tasks.
• AMD SEV-SNP or Intel TDX: Providing hardware-backed security for data and processes.

By combining these technologies, GPU-Enabled TEEs protect sensitive data while delivering high-performance AI processing.

Oasis Runtime Offchain Logic (ROFL): A Game Changer

ROFL, developed by Oasis Labs, is a framework that allows complex logic to run offchain while maintaining security and verifiability. When paired with GPU-Enabled TEEs, ROFL offers:

• Provenance for AI models: Transparent details about how AI models are built and trained.
• Onchain publishing: Ensures that provenance data is publicly accessible and tamper-proof.
• Privacy preservation: Enables AI training and inference on sensitive data without exposing it.

Experiment: Fine-Tuning Large Language Models (LLMs) in a GPU-Enabled Trusted Execution Environment (TEE)

This experiment demonstrates how fine-tuning a large language model (LLM) within a GPU-enabled Trusted Execution Environment (TEE) can ensure the model’s provenance is verified and its metadata published onchain for transparency and trust.

Setting Up the Trusted Virtual Environment

1. Hardware Setup

• GPU Configuration: The system uses an NVIDIA H100 GPU equipped with NVIDIA’s nvtrust security module to enable a secure processing environment.
• Confidential Virtual Machine (CVM): The experiment is powered by AMD SEV-SNP technology, providing end-to-end encryption for data-in-use and securing the entire virtual machine.

2. Verification of Security

• CVM Integrity Check: At boot-up, the cryptographic hashes of the CVM are validated to ensure the environment is secure and uncompromised.
• GPU Security Validation: The integrity of the GPU is confirmed, ensuring it operates within a fully trusted setup. This prevents unauthorized tampering during the fine-tuning process.

Fine-Tuning the Model

Base Model:

• Meta Llama 3 8B Instruct, a cutting-edge language model optimized for instruction-based tasks.

Libraries and Tools:

• Hugging Face Transformers: A versatile library for training and deploying transformer-based models.
• Parameter-Efficient Fine-Tuning (PEFT): A library designed to minimize the resource overhead for adapting large models to new tasks.

Fine-Tuning Technique:

• Low-Rank Adaptation (LoRA): A lightweight fine-tuning method that reduces memory and compute requirements, making it ideal for secure environments.

Experimental Results

• Execution Time:Within the CVM: 30 seconds (average). On a non-secure host: 12 seconds (average).
• Security vs. Speed Trade-Off:The CVM introduces additional latency due to security processes but ensures unmatched transparency and integrity during fine-tuning.

Publishing Provenance Onchain with ROFL

A critical aspect of the experiment is using the ROFL framework (Record of Fidelity and Lineage) to publish the AI model’s provenance onchain, ensuring its authenticity and lineage are verifiable by anyone.

1. Attestation Validation

• The cryptographic chain of trust is verified, starting from the AMD root key to the Versioned Chip Endorsement Key (VCEK).
• The attestation report is cross-checked with the model’s metadata to confirm that both are genuine.

2. Onchain Publishing Process

• A cryptographic hash of the fine-tuned model and training data is recorded on the Sapphire smart contract.
• This allows anyone to verify the model’s authenticity and trace its lineage back to the original fine-tuning process.

3. Benefits of Onchain Provenance

• Transparency: Users gain confidence in the origin and integrity of AI models.
• Community Empowerment: Developers can collaborate, share resources, and innovate using verified models.

Decentralized Marketplaces for AI

Publishing model provenance onchain lays the groundwork for decentralized AI marketplaces, enabling:

1. Verified AI Models: Users can access models with proven transparency and integrity.
2. Fair Compensation: Developers are rewarded for sharing models and training data.
3. Encouraged Collaboration: Privacy and security measures foster data sharing and innovation.

These marketplaces could create a virtuous innovation cycle, where:

• Verified models attract better data and collaborations.
• New data leads to further model improvements.
• Developers and users benefit equally from these advancements.

The Future of Transparent AI

This experiment represents just the beginning of a movement toward trustworthy AI development. Advancements in technologies like GPU-Enabled TEEs and ROFL can:

1. Simplify Adoption:Full support for Intel TDX can eliminate the need for configuring complex CVM stacks.
2. Expand Privacy Capabilities:Enable secure AI training and inference on sensitive data.
3. Accelerate Innovation:Create modular frameworks that simplify the development and deployment of AI applications.

By combining trust, privacy, and transparency, these technologies are poised to revolutionize the way AI is developed, shared, and consumed.

Conclusion

The integration of GPU-enabled TEEs and ROFL technology creates a transparent and secure environment for fine-tuning and verifying AI models. This paradigm fosters a decentralized ecosystem where collaboration thrives, innovation accelerates, and trust is prioritized.

This is the future of AI: transparent, collaborative, and trustworthy. Stay connected with Oasis Labs to explore more groundbreaking advancements and shape the next generation of AI innovation.