Q4 Quant Benchmarks Crown Top Quants

#quantization#kld#perplexityqwen3.5-35b-a3bqwen3.5-35b-a3baessedaiubergarmunsloth

💡Data-driven Q4 quant picks for Qwen3.5-35B: save VRAM, keep quality

⚡ 30-Second TL;DR

What Changed

KLD measures quantization drift from BF16 baseline; lower is better

Why It Matters

Guides practitioners to optimal quants, saving VRAM while preserving model quality for local inference.

What To Do Next

Download AesSedai_Qwen3.5-35B-A3B-IQ4_XS for top efficiency quant.

Who should care:Developers & AI Engineers

Background and context from public sources — not the original article. 7 sources cited.

•Qwen3.5-35B-A3B employs a Gated Deltanet architecture with 75% linear attention layers, drastically reducing KV cache memory and enabling high throughput at long context lengths.[1]
•Unsloth's UD-Q4_K_XL and UD-Q3_K_XL quantizations of larger Qwen3.5-397B-A17B retain 80.5-80.7% accuracy on a 750-prompt benchmark, with only 3.5-4.3% relative error increase over BF16.[3]
•Qwen3.5-35B-A3B activates only ~3B of its 35B total parameters per token via MoE routing, achieving up to 5x higher throughput than dense 27B models despite similar intelligence levels.[2]

📊 Competitor Analysis▸ Show

•Gated Deltanet (linear attention) used in 75% of layers to minimize KV cache size and support long contexts with low memory overhead.[1]
•MoE routing activates ~3B parameters per token out of 35B total, balancing broad knowledge with compute efficiency akin to a 3B dense model.[2]
•Unsloth GGUF quantizations (e.g., UD-Q4_K_XL) optimized with iMatrix for Qwen3.5 series, enabling 3-bit runs on 192GB RAM or 4-bit on 256GB setups for larger variants.[3]

IQ4_XS and Q4_K_M will become standard for 35B MoE on 16GB consumer GPUs

Benchmarks show these quants fit within 16GB VRAM while preserving low KLD and strong PPL, as validated in Q4 community tests.[article]

Qwen3.5 MoE models will dominate local inference over dense counterparts

5x throughput gains from sparse activation combined with quantization robustness enable practical deployment on edge hardware.[2][1]

2026-02

Alibaba releases Qwen3.5 medium series including 35B-A3B MoE, 27B dense, and 122B-A10B.

2026-02

Unsloth publishes GGUF quantizations and benchmarks for Qwen3.5 models.

2026-02-25

YouTube benchmark demonstrates quantized Qwen3.5 variants running on 16GB GPUs.

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

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