Llama3.1 8B Quantization Analysis

Quantized Llama3.1 8B Model Performance Comparison

This analysis compares various quantization methods for the Llama3.1 8B model across multiple benchmarks. The data shows perplexity on WikiText-2, accuracy on 5 zero-shot tasks, and 5-shot accuracy on MMLU.

Graph A: FP16 vs 3.25 wbits Quantization

Key Takeaways:

Strengths

  • Massive compression from 16-bit to 3.25-bit (approximately 80% reduction in model size)
  • HIGGS (p=4) achieves the best performance among 3.25-bit models with 66.36 average score
  • PiQA and Wino tasks maintain relatively high performance despite compression

Weaknesses

  • Significant performance drop across most metrics compared to FP16
  • HellaS benchmark shows the largest degradation (60.01 → 54.92-57.01)
  • Wiki2 perplexity increases substantially, indicating lower language modeling quality

Notable Observations

HIGGS (p=4) at 3.25 wbits delivers the best balance of compression and performance, retaining about 95.7% of FP16's average score while using only 20.3% of the original bit width. This demonstrates significant potential for deploying large language models in resource-constrained environments.

Graph B: FP16 vs 4.0/4.02 wbits Quantization

Key Takeaways:

Strengths

  • Better performance retention than 3.25-bit models across all metrics
  • HIGGS (p=3) at 4.02 wbits achieves 68.73 average score, nearly matching FP16
  • ArcE and PiQA benchmarks show minimal degradation from FP16 performance

Weaknesses

  • Still shows performance gaps in Wiki2 perplexity and ArcC accuracy
  • HellaS benchmark performance still trails FP16 by several points
  • 25% higher bit width compared to 3.25-bit models increases memory requirements

Notable Observations

HIGGS (dyn data-free) at 4.0 wbits achieves 68.29 average score, maintaining about 98.5% of FP16's performance while using 25% of the original bit width. This represents an excellent trade-off between model size and performance for most practical applications, particularly in systems with moderate resource constraints.

Graph C: FP16 vs 4.25/4.26 wbits Quantization

Key Takeaways:

Strengths

  • Closest performance to FP16 while maintaining significant compression
  • HIGGS (p=2) at 4.26 wbits achieves 68.96 average score, only 0.35 below FP16
  • ArcE and PiQA metrics nearly match FP16 performance

Weaknesses

  • Small but consistent gap in Wiki2 perplexity compared to FP16
  • HellaS benchmark still shows noticeable performance degradation
  • Requires more memory compared to lower bit-width quantization methods

Notable Observations

At 4.25/4.26 wbits, most quantization methods achieve above 99% of FP16's average performance while using only 26.6% of the original bit width. HIGGS (p=2) particularly stands out with exceptional performance across all benchmarks. These models represent the most production-ready quantized versions with minimal quality trade-offs.

Summary and Conclusions

Our analysis of Llama3.1 8B quantization methods reveals several important insights:

  1. Compression vs. Performance Trade-off: There is a clear correlation between bit width and performance. As bit width increases from 3.25 to 4.25, the performance gap with FP16 narrows significantly.
  2. Method Effectiveness: HIGGS variants consistently outperform other quantization methods at the same bit width, with HIGGS (p=3) and HIGGS (p=2) showing particularly strong results at 4.02 and 4.26 bits respectively.
  3. Task-Specific Sensitivity: Some benchmarks (like HellaS and ArcC) are particularly sensitive to quantization, while others (like PiQA and ArcE) maintain high performance even under aggressive compression.
  4. Optimal Balance: For most production scenarios, 4.0/4.02-bit models offer the best balance between compression and performance, while 4.25/4.26-bit models are suitable for applications requiring near-FP16 quality.

These findings suggest that quantization can effectively reduce the deployment footprint of large language models while maintaining most of their capabilities. The choice of quantization method and bit width should be guided by specific application constraints and performance requirements.