Summary of Qwen3.5 GGUF Evaluations + My Evaluation Method

2026-03-10 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, medium

Summary

This article evaluates the performance of various GGUF quantized versions of Qwen3.5 LLMs, ranging from 9B to 397B parameters, addressing the common issue of poorly evaluated quantized models. The author details a methodology for GGUF evaluation using a subsampled set of benchmarks including LiveCodeBench, GPQA Diamond, MMLU-Pro, and Math500, executed on a GH200 superchip rented for $1.49/hour. The evaluation focuses on "Relative Error Increase" to interpret performance degradation. Key findings indicate Qwen3.5 397B is highly robust to quantization, with Unsloth's UD IQ2_M being a safe option. For smaller Qwen3.5 models, Q2 quantization is generally advised against, while Q4 is deemed very safe. The study also reveals that abliterated Qwen3.5 9B GGUFs significantly underperform even Q4_K_L versions.

Key takeaway

For NLP Engineers deploying Qwen3.5 GGUF models, you should prioritize Q4 quantization for smaller models and consider Unsloth's UD IQ2_M for the 397B variant to balance memory reduction and accuracy. Avoid Q2 quantization due to significant performance degradation, and be wary of abliterated models which often sacrifice accuracy for uncensoring. Always evaluate your chosen GGUF with task-specific benchmarks, not just PPL/KLD, to ensure it meets your application's reliability needs.

Key insights

Quantized GGUF LLMs require rigorous evaluation beyond perplexity to understand real-world performance degradation.

Principles

• Reasoning benchmarks are sensitive to quantization errors.
• Quantization can blur knowledge in models.
• Relative error increase clarifies performance impact.

Method

Evaluate GGUF models using subsampled standard benchmarks (LiveCodeBench, GPQA Diamond, MMLU-Pro, Math500) on a GH200 superchip, focusing on "Relative Error Increase" to quantify degradation.

In practice

• Avoid Q2 quantization for Qwen3.5 models.
• Q4 quantization is generally safe for Qwen3.5.
• Unsloth's UD IQ2_M is safe for Qwen3.5 397B.

Topics

• GGUF Format
• LLM Quantization
• Model Evaluation Benchmarks
• Qwen3.5 Models
• Quantization Impact Analysis

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Summary of Qwen3.6 GGUF Evals (Updating...) — Qwen3.6 27B significantly improves accuracy over Qwen3.5, while DeepSeek-V4 introduces efficient long-context MoE architecture.
• Lessons from GGUF Evaluations: Ternary Qwen3.5, Bricked Minimax — Model robustness to quantization varies significantly, necessitating empirical evaluation beyond perplexity metrics.
• Does it make sense to use alternative quantizations of QAT models? [D] — QAT models are optimized for specific quantization schemes; alternative methods can degrade performance.
• Qwen-3.5 PLUS Outperforms MoE 397B-A17B — Qwen 3.5 Plus demonstrates strong causal reasoning but can generate invalid optimizations; the base Qwen 3.5 struggles with complex validation.
• Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment — Fidelity metrics like KLD are unreliable proxies for LLM benchmark quality, especially in near-baseline performance zones.
• Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7 — Local, quantized models can surprisingly outperform larger proprietary models in specific creative tasks.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.