TurboQuant vs GPTQ vs AWQ: Which Should You Use?

Choosing the right quantization method depends on your priorities: accuracy, speed, memory efficiency, or ease of deployment.

Accuracy Comparison

TurboQuant achieves the best accuracy at low bit widths thanks to its near-Shannon distortion rate and QJL bias correction. At 3-bit, PPL degradation is under 1% compared to FP16.

Speed Comparison

TurboQuant's Triton kernel implementation achieves 8x attention speedup on H100 compared to FP16, significantly outperforming GPTQ (2.1x) and AWQ (2.0x).

When to Use What

• TurboQuant: Best overall. Use when you want maximum speed + accuracy at low bits.
• GPTQ: Good for 4-bit weight quantization. Requires calibration data.
• AWQ: Similar to GPTQ but with activation-aware scaling. Also requires calibration.
• FP8: Easiest to deploy if your GPU supports it (H100+).