90+ Experiments
Confiance : high
90-experimentssystematic-experimentationflash-moeoptimization-methodologyai-human-collaboration24-hour-developmentquantization-analysisperformance-benchmarkingqwen3.5-397btechnical-validation
Systematic experimentation methodology demonstrated in the flash-moe project, where over 90 optimization experiments were conducted within a 24-hour-development cycle. Represents a structured approach to technical optimization through ai-human-collaboration.
Experimental Scope
The 90+ experiments encompassed multiple optimization dimensions:
Quantization Strategies:
- 4-bit vs 2-bit expert quantization
- Different quantization algorithms and settings
- Trade-offs between model size and quality
Performance Optimization:
- fma-kernels implementation variants
- Metal shader optimization approaches
- Memory management strategies
Architecture Variants:
- ssd-streaming pipeline configurations
- pure-c-metal implementation alternatives
- GPU utilization patterns
Methodology
Systematic Exploration:
- Structured parameter space exploration
- Quantitative validation of each approach
- Documentation of both successes and failures
Rapid Iteration:
- Quick experiment turnaround enabled by AI assistance
- Automated benchmarking and validation
- Continuous refinement based on results
Quality Validation:
- Performance metrics for each configuration
- tool-calling-reliability assessment
- production-quality validation
Key Findings
The experimental process revealed:
- Quality Cliff: Sharp degradation at 2-bit quantization affecting JSON formatting
- FMA Optimization: Measurable performance improvement (3.90 → 4.36 tok/s)
- Storage Trade-offs: 209GB (4-bit) vs 120GB (2-bit) with quality implications
Innovation Value
The systematic experimental approach enabled:
- Comprehensive Optimization: Thorough exploration of solution space
- Evidence-Based Decisions: Quantitative basis for technical choices
- Documented Learning: Reusable insights from failed experiments
- Breakthrough Performance: Results exceeding individual optimization attempts
Implications
90+ experiments in 24 hours suggests:
- AI-human collaboration can dramatically accelerate experimentation
- Systematic approaches can compress traditional R&D timelines
- Comprehensive validation is possible within compressed development cycles
- Failed experiments provide valuable learning when properly documented
See also
- flash-moe - Project implementing systematic experimentation
- 24-hour-development - Compressed timeline enabling intensive experimentation
- ai-human-collaboration - Collaboration model enabling rapid iteration
- quality-cliff - Key finding from experimental validation