GPU Optimization Guide

Overview

AIOSx supports GPU optimization across NVIDIA, AMD, and Intel GPUs. The UAICP GPU layer provides unified abstraction for managing GPU resources and optimizing workload placement.

Supported GPU Providers

NVIDIA

• Models: A100, H100, A10, T4
• Capabilities: CUDA, TensorRT
• Best For: High-performance AI workloads, LLM inference

AMD

• Models: MI250, MI210, RX 7900 XTX
• Capabilities: ROCm
• Best For: Cost-effective AI workloads, training

Intel

• Models: Ponte Vecchio, Arc A770
• Capabilities: oneAPI
• Best For: Emerging workloads, cost optimization

Configuration

GPU Provider Setup

Edit config/gpu_providers.yaml:

yaml
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gpu_providers:
  nvidia:
    enabled: true
    credentials:
      api_key: "${NVIDIA_API_KEY}"
    default_models:
      - A100
      - H100
      - A10
      - T4
  
  amd:
    enabled: true
    credentials:
      api_key: "${AMD_API_KEY}"
    default_models:
      - MI250
      - MI210
      - RX 7900 XTX
  
  intel:
    enabled: true
    credentials:
      api_key: "${INTEL_API_KEY}"
    default_models:
      - Ponte Vecchio
      - Arc A770

Environment Variables

bash
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export NVIDIA_API_KEY="your-nvidia-key"
export AMD_API_KEY="your-amd-key"
export INTEL_API_KEY="your-intel-key"

Usage

Listing Available GPUs

python
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# Via API
GET /uaicp/gpus

# Response
{
  "providers": ["nvidia", "amd", "intel"],
  "gpus": [...],
  "total_providers": 3,
  "total_gpus": 9
}

GPU Allocation

python
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# Allocate GPU for workload
POST /uaicp/workloads/route
{
  "task_id": "task_123",
  "task_type": "llm_inference",
  "requirements": {
    "gpu_required": true,
    "gpu_preference": "nvidia",  # Optional
    "preferred_model": "A100",   # Optional
    "min_memory_gb": 80,
    "max_cost_per_hour": 5.0
  }
}

GPU Arbitrator

The GPU arbitrator automatically selects the best GPU based on:

1. Performance: Highest performance score within constraints
2. Cost: Lowest cost while meeting performance requirements
3. Memory: Sufficient memory for workload
4. Latency: Lowest latency for inference workloads
5. Availability: Only considers available GPUs

Manual GPU Selection

python
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from aiosx.uaicp.gpu.gpu_registry import GPURegistry
from aiosx.uaicp.gpu.nvidia_provider import NVIDIAProvider

# Register provider
gpu_registry = GPURegistry()
nvidia_provider = NVIDIAProvider()
gpu_registry.register_provider(nvidia_provider)

# List GPUs
gpus = await gpu_registry.list_all_gpus()

# Find best GPU
best_gpu = await gpu_registry.find_best_gpu(
    requirements={
        "preferred_provider": "nvidia",
        "min_memory_gb": 80,
        "max_cost_per_hour": 5.0
    }
)

GPU Benchmarking

python
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from aiosx.uaicp.gpu.gpu_arbitrator import GPUArbiter

# Compare GPUs for workload type
comparison = await gpu_arbitrator.compare_gpus(
    gpu_ids=["nvidia-a100-1", "amd-mi250-1", "intel-pvc-1"],
    workload_type="llm_inference"
)

# Response includes performance scores and recommendations

Performance Characteristics

NVIDIA GPUs

AMD GPUs

Intel GPUs

Best Practices

1. Workload Matching: Match GPU to workload requirements

   • LLM inference: High memory (A100, H100, MI250)
   • Training: High compute (H100, MI250)
   • Light inference: Cost-effective (A10, T4, RX 7900)

2. Cost Optimization: Use GPU arbitrator for automatic cost optimization

3. Multi-Provider: Leverage multiple providers for redundancy

4. Benchmarking: Benchmark GPUs for specific workload types

5. Monitoring: Monitor GPU utilization and performance

Integration with Resource Arbiter

The Resource Arbiter integrates GPU selection with other resources:

python
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# Allocate complete resource stack
allocation = await resource_arbiter.allocate_resources(
    task_id="task_123",
    requirements={
        "gpu_required": True,
        "cpu_required": True,
        "model_required": True,
        "gpu_preference": "nvidia",
        "cost_constraint": 10.0
    }
)

Troubleshooting

GPU Not Available

1. Check provider credentials
2. Verify GPU availability in region
3. Check cost constraints
4. Review memory requirements

Performance Issues

1. Benchmark GPU for specific workload
2. Check GPU utilization
3. Verify workload-GPU compatibility
4. Consider alternative GPU models

High Costs

1. Use cost constraints in allocation
2. Consider AMD or Intel for cost savings
3. Use lower-tier models for non-critical workloads
4. Optimize with ROI engine