4.7 KiB
Qwen3-8B Quantization Comparison Summary
Q3_HIFI (Adaptive/Custom)
Pros:
- 🏆 Best quality with lowest perplexity of 10.56 (4.4% better than Q3_K_M, 7.2% better than Q3_K_S)
- 📦 Smaller than Q3_K_M (3.72 vs 3.84 GiB) while being significantly better quality
- 🎯 Uses intelligent layer-sensitive quantization (Q3_HIFI on sensitive layers, mixed q3_K/q4_K elsewhere)
- 📊 Most consistent results (lowest relative standard deviation in perplexity)
Cons:
- 🐢 Slowest inference at 143.98 TPS (6.3% slower than Q3_K_S)
- 🔧 Custom quantization may have less community support
Best for: Production deployments where output quality matters, tasks requiring accuracy (reasoning, coding, complex instructions), or when you want the best quality-to-size ratio.
Performance Comparison (Q3_HIFI vs the others)
Q3_K_M
| Metric | Q3_HIFI | Q3_K_M | Difference |
|---|---|---|---|
| Speed (TPS) | 143.98 | 144.72 | -0.74 (0.5% slower) |
| Perplexity | 10.56 | 11.05 | -0.49 (4.4% better) |
| File Size | 3.72 GiB | 3.84 GiB | -0.12 GiB (3.1% smaller) |
| Bits Per Weight | 3.90 | 4.02 | -0.12 (3.0% less) |
Pros:
- ⚖️ Traditional "balanced" approach between speed and quality
- 📚 Well-documented, standard quantization method
Cons:
- 💾 Largest file size at 3.84 GiB despite not being the best quality
- 🐌 Middle-of-the-road speed (144.7 TPS)
- ❌ Outclassed by Q3_HIFI which is smaller AND better quality
Best for: Legacy compatibility or when you need a proven, standard quantization approach. Summary: Q3_HIFI delivers significantly better quality (4.4% lower perplexity) in a smaller package (3.1% less storage) with virtually no speed penalty (0.5% slower).
Q3_K_S
| Metric | Q3_HIFI | Q3_K_S | Difference |
|---|---|---|---|
| Speed (TPS) | 143.98 | 153.74 | -9.76 (6.3% slower) |
| Perplexity | 10.56 | 11.38 | -0.82 (7.2% better) |
| File Size | 3.72 GiB | 3.51 GiB | +0.21 GiB (6.0% larger) |
| Bits Per Weight | 3.90 | 3.68 | +0.22 (6.0% more) |
Pros:
- ⚡ Fastest inference at 153.74 TPS (~6% faster than Q3_K_M, ~7% faster than Q3_HIFI)
- 💾 Smallest file size at 3.51 GiB
- ✅ Best choice when speed and storage are critical
Cons:
- ❌ Worst quality with perplexity of 11.38 (7.2% higher than Q3_HIFI)
- Uses only q3_K quantization throughout (no mixed precision)
Best for: Resource-constrained environments, real-time applications where latency matters more than accuracy, or initial prototyping. Summary: Q3_HIFI trades a 6.3% speed reduction and 6.0% larger file size for a substantial 7.2% improvement in quality (lower perplexity).
Recommendation Matrix
| Priority | Recommended Model | Rationale |
|---|---|---|
| Quality First | Q3_HIFI | 7.2% better perplexity than Q3_K_S with acceptable speed loss |
| Speed First | Q3_K_S | 6.3% faster inference, acceptable quality tradeoff for latency-sensitive apps |
| Best Balance | Q3_HIFI | Better quality AND smaller size than Q3_K_M, only 0.5% slower |
| Smallest Size | Q3_K_S | 6% smaller than alternatives |
Key Insight
Q3_HIFI represents a clear advancement over the traditional Q3_K_M approach. It achieves:
- 4.4% lower perplexity (better accuracy)
- 3.1% smaller file size (3.72 vs 3.84 GiB)
- Only 0.5% slower inference (144.0 vs 144.7 TPS)
The Q3_K_M quantization is essentially obsoleted by Q3_HIFI for most use cases. The only remaining choice is between Q3_K_S (maximum speed, acceptable quality) and Q3_HIFI (maximum quality, acceptable speed).
Appendix (Test Environment Details)
| Component | Specification |
|---|---|
| OS | Ubuntu 24.04.3 LTS |
| CPU | AMD EPYC 9254 24-Core Processor |
| CPU Cores | 96 cores (2 threads/core) |
| RAM | 1.0Ti |
| GPU | NVIDIA L40S × 2 |
| VRAM | 46068 MiB per GPU |
| CUDA | 12.9 |