Optimize LLM Command

Get quick, actionable recommendations for LLM serving optimization.

Usage

/sd:optimize-llm [focus]

Arguments

• focus (optional): Optimization priority
  • latency - Focus on reducing response time
  • cost - Focus on reducing inference costs
  • throughput - Focus on maximizing requests/second
  • If omitted: Provide balanced recommendations

Examples

/sd:optimize-llm
/sd:optimize-llm latency
/sd:optimize-llm cost

Workflow

1. Gather Context

   • Search for LLM-related configuration files
   • Look for: model configs, serving configs, inference scripts
   • Identify current serving stack (vLLM, TGI, TensorRT-LLM, etc.)

2. Spawn LLM Optimization Advisor Agent Use the llm-optimization-advisor agent to analyze and provide recommendations. The agent specializes in:

   • Quantization strategies (INT8, INT4, FP16)
   • Batching optimization (continuous, dynamic)
   • KV cache optimization (PagedAttention)
   • Serving framework selection
   • Cost reduction strategies

3. Present Recommendations Display optimization opportunities organized by:

   • Quick Wins - Low effort, high impact changes
   • Medium Effort - Moderate changes with significant benefits
   • Advanced - Architectural changes for maximum performance

Output Format

## LLM Optimization Report

### Current Setup
- Model: [detected or ask]
- Framework: [detected or unknown]
- Hardware: [detected or ask]

### Quick Wins
1. [Optimization] - [Expected impact]
2. ...

### Medium Effort Optimizations
1. [Optimization] - [Expected impact]
2. ...

### Advanced Optimizations
1. [Optimization] - [Expected impact]
2. ...

### Estimated Total Impact
- Latency: [X]% improvement
- Cost: [X]% reduction
- Throughput: [X]x increase