View Benchmark Results

Overview

Flow ID: view-benchmark-results
Category: Performance Benchmarking
Estimated Duration: 2-5 minutes
User Role: All Users
Complexity: Simple

Purpose: Review completed benchmark test results to understand model performance characteristics including speed, latency, throughput, and resource usage across various test scenarios.

Trigger

What initiates this flow:

• User manually initiates

Specific trigger: Benchmark completed, user wants to analyze performance data.

User Intent Analysis

Primary Intent

Understand model performance through completed benchmark results to make informed decisions about model selection and configuration.

Secondary Intents

• Identify optimal settings
• Compare models
• Document system capabilities
• Troubleshoot performance issues

Step-by-Step Flow

Main Path (Happy Path)

Step 1: Navigate to Benchmarking

• User Action: Settings > Benchmarking tab
• System Response: Benchmark page loads
• UI Elements Visible:
  • System info panel (left)
  • Results tables (right)
  • Benchmark status: “Completed” with completion time

Step 2: Review System Information

• User Action: Check system stats in left panel
• UI Elements Visible:
  • OS, CPU, RAM, GPU info
  • Benchmark completion time
  • Duration
  • Tests completed count

Step 3: Examine Model Results Tables

• User Action: Browse results for each tested model
• UI Elements Visible:
  • Model section header with model name
  • Expandable test categories
  • Each category shows aggregate time

Step 4: Expand Test Category

• User Action: Click category row to expand (e.g., “Context Size Tests”)
• System Response: Category expands showing individual tests
• UI Elements Visible:
  • Parent row showing category
  • Child rows showing each test:
    • Test name (e.g., “Context 4096, 25% tokens”)
    • Status badge (Completed, Error, etc.)
    • Load time
    • First token time
    • Generation time
    • Input/output tokens
    • Tokens per second
    • CPU load

Step 5: Analyze Performance Metrics

• User Action: Review numbers to understand performance
• System Response: Data displayed in organized table
• Key Metrics to Review:
  • Tokens per second: Higher is better (speed)
  • First token time: Lower is better (responsiveness)
  • Load time: One-time cost per model start
  • CPU load: Resource usage percentage

Step 6: Compare Test Results

• User Action: Compare results across different test scenarios
• System Response: Can scroll to compare numbers
• Insights Possible:
  • Which context sizes perform best
  • Speed vs. quality trade-offs
  • Worker count impact on embeddings
  • Memory allocation effects

Final Step: Results Reviewed

• Success Indicator:
  • Understood model performance
  • Identified optimal configurations
  • Can make informed decisions
• Next Actions:
  • Adjust settings based on findings
  • Export results for documentation
  • Run additional benchmarks
  • Configure chat settings optimally

Error States & Recovery

QA Note: Viewing completed results is read-only with no error conditions.

Pain Points & Friction

Identified Issues:

1. Overwhelming Data Presentation
   • Impact: Many numbers without context
   • Potential Improvement: Summaries, interpretations, recommendations

Design Considerations

Following Contextual Design Principles:

1. Simplification Opportunities: Summary view with key insights
2. User Trust: Accurate, complete results
3. Cognitive Load: Visual aids to interpret numbers

Related Flows

• Run Full Benchmark Suite - Generate results
• Export Benchmark Results - Save results

Technical References

Knowledge Base Sections:

• src/components/benchmarking/benchmark-results.js - Results display
• src/components/benchmarking/llm-results-table.js - LLM results
• src/components/benchmarking/embedding-results-table.js - Embedding results

Version History

Notes

Results Interpretation:

• Good tokens/sec varies by hardware; 20-50 typical for consumer GPUs
• Lower CPU load better if performance adequate
• Compare across tests to find sweet spots

Best Practices:

• Focus on metrics relevant to your use case
• Compare tests to identify optimal context window
• Note resource usage patterns

Common User Questions:

• “What’s a good tokens per second rate?” - Varies by hardware; compare to your own baselines
• “Should all tests pass?” - Some tests may fail if exceeding hardware limits; this is informative