> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pinai.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Private LLM Inference

Trusted Execution Environments (TEEs) enable secure and private large language model (LLM) inference, ensuring that sensitive computations occur within an encrypted and isolated environment.

## Core functionality

The core functionality focuses on securely performing privacy-preserving computations, including private LLM inference, while ensuring data confidentiality and integrity at every stage of execution.

* **Secure model loading**: Loads encrypted models into the TEE, preventing unauthorized access.
* **Protected inference**: Runs entire inference workflows securely within the enclave.
* **Secure output handling**: Ensures results remain encrypted and protected during transmission and storage.

## Key components

The key components enable the secure management and execution of model inference workflows, ensuring efficiency, accuracy, and robust protection throughout the process.

### Model management

* **Encrypted model storage**: Stores models in encrypted format, preventing unauthorized access.
* **Secure weight loading**: Loads model weights securely into the enclave to ensure integrity and authenticity.
* **Version control & updates**: Implements secure version tracking and controlled updates to prevent tampering.

### Inference pipeline

* **Input preprocessing in TEE**: Cleans, formats, and normalizes incoming data before inference.
* **Batched inference processing**: Groups multiple requests to optimize efficiency and resource usage.
* **Output post-processing**: Ensures results remain encrypted, preventing data leaks or unauthorized exposure.

### Security measures

* **Model encryption at rest**: Encrypts model weights before and after inference to prevent unauthorized access.
* **Secure inference runtime**: Ensures that inference operations only execute within verified TEEs.
* **Memory protection**: Prevents unauthorized memory access, mitigating side-channel attacks.

*The following Go structs define the TEE-based LLM inference architecture, ensuring secure model execution and data protection.*

```go theme={null}
type LLMInference struct {
    ModelID     string
    ModelConfig ModelSettings
    Runtime     TEERuntime
}

type ModelSettings struct {
    // Model configuration
    BatchSize      int
    Precision      string
    MaxInputLength int
    // Security settings
    EncryptionKey  []byte
    AccessControl  Policy
}

type InferenceRequest struct {
    // Input data for inference
    Input []byte
    // Metadata for processing
    Metadata RequestMetadata
    // Security context
    SecurityContext Context
}
```