NVIDIA

NVIDIA provides enterprise-grade AI inference services, leveraging its powerful GPU technology stack to provide high-performance support for large-scale AI applications.

Supported Models

LLaMA Series

• nv-llama2-70b - LLaMA 2 70B model
• nv-llama2-13b - LLaMA 2 13B model
• nv-llama2-7b - LLaMA 2 7B model

Mistral Series

• nv-mistral-7b - Mistral 7B model
• nv-mixtral-8x7b - Mixtral 8x7B model

Other Models

• nv-code-llama-34b - Code LLaMA model
• nv-yi-34b - Yi 34B model

Configuration

Basic Configuration

Configure in config.yaml or ~/.bytebuddy/config.yaml:

models:
  - name: "nvidia-llama"
    provider: "nvidia"
    model: "nv-llama2-70b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["chat", "edit"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4096

Enterprise Configuration

models:
  - name: "nvidia-enterprise"
    provider: "nvidia"
    model: "nv-mixtral-8x7b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["chat", "edit"]
    defaultCompletionOptions:
      temperature: 0.5
      maxTokens: 8192

Multi-Model Configuration

models:
  - name: "nvidia-llama-70b"
    provider: "nvidia"
    model: "nv-llama2-70b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4096

  - name: "nvidia-code"
    provider: "nvidia"
    model: "nv-code-llama-34b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["edit", "apply"]
    defaultCompletionOptions:
      temperature: 0.2
      maxTokens: 2048

Configuration Fields

Required Fields

• name: Unique identifier for the model configuration
• provider: Set to "nvidia"
• model: Model name
• apiKey: NVIDIA API key

Optional Fields

• roles: Model roles [chat, edit, apply, autocomplete]
• defaultCompletionOptions:
  • temperature: Control randomness (0-1)
  • maxTokens: Maximum tokens
  • topP: Nucleus sampling parameter
  • topK: Sampling candidates count
  • repetitionPenalty: Repetition penalty

Environment Variables

# ~/.bashrc or ~/.zshrc
export NVIDIA_API_KEY="your-nvidia-api-key"

Getting API Key

1. Visit NVIDIA API Catalog
2. Register NVIDIA account
3. Generate API key
4. Configure access permissions
5. Save the key to environment variable

Use Case Configurations

High-Performance Inference

models:
  - name: "high-performance"
    provider: "nvidia"
    model: "nv-llama2-70b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["chat", "edit"]
    defaultCompletionOptions:
      temperature: 0.5
      maxTokens: 4096

Code Generation

models:
  - name: "code-gen"
    provider: "nvidia"
    model: "nv-code-llama-34b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["edit", "apply"]
    defaultCompletionOptions:
      temperature: 0.2
      maxTokens: 2048

Fast Response

models:
  - name: "fast-inference"
    provider: "nvidia"
    model: "nv-mistral-7b"
    apiKey: "${NVIDIA_API_KEY}"
    roles: ["chat", "autocomplete"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2048

GPU Optimization Features

TRITON Inference Server

NVIDIA uses TRITON inference server to optimize model performance, providing:

• Batch processing optimization
• Dynamic batching
• Model concurrency

TensorRT Acceleration

• FP16/INT8 precision optimization
• Layer fusion optimization
• Kernel auto-tuning

Performance Metrics

Inference Speed

• NIM Optimization: Up to 3x inference acceleration
• Batch Processing: Supports massive concurrent requests
• Low Latency: Millisecond-level response time

Scalability

• Horizontal Scaling: Supports multi-instance deployment
• Vertical Scaling: Supports dynamic resource adjustment
• Auto-Scaling: Adjusts automatically based on load

Troubleshooting

Common Issues

1. GPU Out of Memory

   • Reduce batch size
   • Use model quantization
   • Increase VRAM resources

2. High Latency

   • Check network connection
   • Optimize batch configuration
   • Enable model caching

3. Low Throughput

   • Increase concurrency
   • Optimize model configuration
   • Scale resources

Debugging Steps

1. Verify API key format and validity
2. Check network connection and firewall settings
3. Monitor GPU utilization
4. View error logs
5. Confirm quotas and limits

Best Practices

1. Model Selection

• Choose appropriate model based on hardware resources
• Consider latency and throughput requirements
• Optimize model precision and speed balance

2. Resource Management

• Monitor GPU utilization
• Optimize batch size
• Allocate memory resources appropriately

3. Security Compliance

• Enable data encryption
• Implement access controls
• Maintain audit logs

4. Performance Optimization

• Enable streaming responses
• Implement request caching
• Use batch processing
• Optimize model loading