DeepSeek V3.2 quants hit near-native performance

Massive Mixture-of-Experts (MoE) models like V3.2 possess a significant "quantization buffer," where extreme parameter redundancy offsets the precision tax of low-bit deployment.

• –Q4_K_M (4-bit) is the gold standard, maintaining near-identical performance to the FP8 baseline in complex coding and math benchmarks
• –Dynamic 3-bit (DQ3_K_M) quants are leveraging specialized weights to outperform older 4-bit V3.1 models in reasoning tasks
• –Critical benchmarks like AIME 2025 and LiveCodeBench show that reasoning-first models (V3.2-Speciale) are more sensitive to quantization below 4-bit than general chat variants
• –The move to native FP8 training means the "base" model is already optimized for the low-precision regimes used in modern inference engines
• –Hardware remains the primary bottleneck, as even a 4-bit quant of the 671B model requires ~380GB of VRAM, mandating 8-GPU H100/A100 clusters