Benjamin Marie(@bnjmn_marie):Qwen3.8-27B NVFP4 variants are very close in accuracy. The real differences are memory and speed. If you're VRAM-limited, minima-ai/mnma_qwen3.8_27b_nvfp4 is a good pick, but it doesn't include MTP for faster inference. NVIDIA's version has MTP, but in my long-context coding tests MTP-4 is only ~2× faster than no MTP, and still ~2.5–3× slower than Unsloth (RTX Pro 6000). A likely reason: NVIDIA quantizes lm_head to NVFP4, while Unsloth keeps it FP8. Since MTP shares the target model's lm_head, this can hurt prediction quality and acceptance rate. So my pick is Unsloth NVFP4. Now testing accuracy on long-horizon agentic coding.

2026.09.13 20:02

Qwen3.8-27B NVFP4 variants are very close in accuracy. The real differences are memory and speed. If you're VRAM-limited, minima-ai/mnma_qwen3.8_27b_nvfp4 is a good pick, but it doesn't include MTP for faster inference. NVIDIA's version has MTP, but in my long-context coding tests MTP-4 is only ~2× faster than no MTP, and still ~2.5–3× slower than Unsloth (RTX Pro 6000). A likely reason: NVIDIA quantizes lm_head to NVFP4, while Unsloth keeps it FP8. Since MTP shares the target model's lm_head, this can hurt prediction quality and acceptance rate. So my pick is Unsloth NVFP4. Now testing accuracy on long-horizon agentic coding.