aitbc/.windsurf/skills/ollama-gpu-testing-skill.md

description, title, version

description	title	version
Atomic Ollama GPU inference testing with deterministic performance validation and benchmarking	ollama-gpu-testing-skill	1.1

Ollama GPU Testing Skill

Purpose

Test and validate Ollama GPU inference performance, GPU provider integration, payment processing, and blockchain recording with deterministic benchmarking metrics.

Activation

Trigger when user requests Ollama GPU testing: inference performance validation, GPU provider testing, payment processing validation, or end-to-end workflow testing.

Input

{
  "operation": "test-gpu-inference|test-payment-processing|test-blockchain-recording|test-end-to-end|comprehensive",
  "model_name": "string (optional, default: llama2)",
  "test_prompt": "string (optional for inference testing)",
  "test_wallet": "string (optional, default: test-client)",
  "payment_amount": "number (optional, default: 100)",
  "gpu_provider": "string (optional, default: aitbc-host-gpu-miner)",
  "benchmark_duration": "number (optional, default: 30 seconds)",
  "inference_count": "number (optional, default: 5)"
}

Output

{
  "summary": "Ollama GPU testing completed successfully",
  "operation": "test-gpu-inference|test-payment-processing|test-blockchain-recording|test-end-to-end|comprehensive",
  "test_results": {
    "gpu_inference": "boolean",
    "payment_processing": "boolean",
    "blockchain_recording": "boolean",
    "end_to_end_workflow": "boolean"
  },
  "inference_metrics": {
    "model_name": "string",
    "inference_time": "number",
    "tokens_per_second": "number",
    "gpu_utilization": "number",
    "memory_usage": "number",
    "inference_success_rate": "number"
  },
  "payment_details": {
    "wallet_balance_before": "number",
    "payment_amount": "number",
    "payment_status": "success|failed",
    "transaction_id": "string",
    "miner_payout": "number"
  },
  "blockchain_details": {
    "transaction_recorded": "boolean",
    "block_height": "number",
    "confirmations": "number",
    "recording_time": "number"
  },
  "gpu_provider_status": {
    "provider_online": "boolean",
    "gpu_available": "boolean",
    "provider_response_time": "number",
    "service_health": "boolean"
  },
  "issues": [],
  "recommendations": [],
  "confidence": 1.0,
  "execution_time": "number",
  "validation_status": "success|partial|failed"
}

Process

1. Analyze

• Validate GPU testing parameters and operation type
• Check Ollama service availability and GPU status
• Verify wallet balance for payment processing
• Assess GPU provider availability and health

2. Plan

• Prepare GPU inference testing scenarios
• Define payment processing validation criteria
• Set blockchain recording verification strategy
• Configure end-to-end workflow testing

3. Execute

• Test Ollama GPU inference performance and benchmarks
• Validate payment processing and wallet transactions
• Verify blockchain recording and transaction confirmation
• Test complete end-to-end workflow integration

4. Validate

• Verify GPU inference performance metrics
• Check payment processing success and miner payouts
• Validate blockchain recording and transaction confirmation
• Confirm end-to-end workflow integration and performance

Constraints

• MUST NOT submit inference jobs without sufficient wallet balance
• MUST validate Ollama service availability before testing
• MUST monitor GPU utilization during inference testing
• MUST handle payment processing failures gracefully
• MUST verify blockchain recording completion
• MUST provide deterministic performance benchmarks

Environment Assumptions

• Ollama service running on port 11434
• GPU provider service operational (aitbc-host-gpu-miner)
• AITBC CLI accessible for payment and blockchain operations
• Test wallets configured with sufficient balance
• GPU resources available for inference testing

Error Handling

• Ollama service unavailable → Return service status and restart recommendations
• GPU provider offline → Return provider status and troubleshooting steps
• Payment processing failures → Return payment diagnostics and wallet status
• Blockchain recording failures → Return blockchain status and verification steps

Example Usage Prompt

Run comprehensive Ollama GPU testing including inference performance, payment processing, blockchain recording, and end-to-end workflow validation

Expected Output Example

{
  "summary": "Comprehensive Ollama GPU testing completed with optimal performance metrics",
  "operation": "comprehensive",
  "test_results": {
    "gpu_inference": true,
    "payment_processing": true,
    "blockchain_recording": true,
    "end_to_end_workflow": true
  },
  "inference_metrics": {
    "model_name": "llama2",
    "inference_time": 2.3,
    "tokens_per_second": 45.2,
    "gpu_utilization": 78.5,
    "memory_usage": 4.2,
    "inference_success_rate": 100.0
  },
  "payment_details": {
    "wallet_balance_before": 1000.0,
    "payment_amount": 100.0,
    "payment_status": "success",
    "transaction_id": "tx_7f8a9b2c3d4e5f6",
    "miner_payout": 95.0
  },
  "blockchain_details": {
    "transaction_recorded": true,
    "block_height": 12345,
    "confirmations": 1,
    "recording_time": 5.2
  },
  "gpu_provider_status": {
    "provider_online": true,
    "gpu_available": true,
    "provider_response_time": 1.2,
    "service_health": true
  },
  "issues": [],
  "recommendations": ["GPU inference optimal", "Payment processing efficient", "Blockchain recording reliable"],
  "confidence": 1.0,
  "execution_time": 67.8,
  "validation_status": "success"
}

Model Routing Suggestion

Fast Model (Claude Haiku, GPT-3.5-turbo)

• Basic GPU availability checking
• Simple inference performance testing
• Quick service health validation

Reasoning Model (Claude Sonnet, GPT-4)

• Comprehensive GPU benchmarking and performance analysis
• Payment processing validation and troubleshooting
• End-to-end workflow integration testing
• Complex GPU optimization recommendations

Coding Model (Claude Sonnet, GPT-4)

• GPU performance optimization algorithms
• Inference parameter tuning
• Benchmark analysis and improvement strategies

Performance Notes

• Execution Time: 10-30 seconds for basic tests, 60-120 seconds for comprehensive testing
• Memory Usage: <300MB for GPU testing operations
• Network Requirements: Ollama service, GPU provider, blockchain RPC connectivity
• Concurrency: Safe for multiple simultaneous GPU tests with different models
• Benchmarking: Real-time performance metrics and optimization recommendations