Qwen 3.5 Local Run Size Poll

#local-hardware#model-sizes#community-pollqwen-3.5qwen-3.5

💡See which Qwen 3.5 size dominates local runs—pick yours by community hardware trends

⚡ 30-Second TL;DR

What Changed

27B model for single-card GPU setups

Why It Matters

Highlights hardware demands for local Qwen 3.5, helping practitioners choose optimal model sizes based on community momentum.

What To Do Next

Check your GPU setup and download the 27B Qwen 3.5 from Hugging Face for single-card testing.

Who should care:Developers & AI Engineers

Background and context from public sources — not the original article. 6 sources cited.

•Qwen3.5 includes sparse models like 35B-A3B (3B active parameters) and 122B-A10B (10B active parameters), outperforming larger predecessors through improved architecture and data quality.
•The series features a flagship 397B-A17B model with 17B active parameters, positioning it as the smallest in the Open-Opus class while competing with models like Kimi's 400B.
•Qwen3.5-27B dense model achieves 72.4 on SWE-bench Verified, tying GPT-5 mini, and excels in agentic benchmarks like BFCL-V4 (72.2) for the 122B variant.
•Hosted Qwen3.5-Plus offers a 1M context window and built-in tools via Alibaba Cloud, with pricing starting at $0.10 per million tokens for Flash.

📊 Competitor Analysis▸ Show

Feature/Benchmark	Qwen3.5-27B	Qwen3.5-35B-A3B	Qwen3.5-122B-A10B	GPT-5 mini	Claude Sonnet 4.5
SWE-bench Verified	72.4	-	-	72.4	-
BFCL-V4	-	-	72.2	-	-
Instruction following (IFEval)	-	-	93.4	93.9	-
Pricing (Flash)	$0.10/M	-	-	-	-

•Qwen3.5-35B-A3B: 35B total parameters, 3B active (sparse MoE-like routing per token), runs on 8GB+ VRAM GPUs with GGUF quantization.
•Qwen3.5-122B-A10B: 122B total, 10B active parameters, leads in agentic tasks (BFCL-V4: 72.2, BrowseComp: 63.8, Terminal-Bench 2: 49.4).
•Qwen3.5-397B-A17B: 397B total, 17B active parameters, ~4.3% sparsity ratio, native multimodality and spatial intelligence features.
•Qwen3.5-27B: Dense model, competitive in coding (tops charts in local benchmarks) and medium-sized evaluations.

Qwen3.5 sparse models will dominate local inference on consumer hardware.

Models like 35B-A3B run efficiently on 8GB VRAM while surpassing larger dense predecessors in benchmarks.

Chinese labs like Qwen will close the gap with Western SOTA in agentic tasks by mid-2026.

Qwen3.5-122B leads open-source agent benchmarks, following refreshes from Z.ai, Minimax, and Kimi.

2026-02

Qwen3.5 series released, including 27B dense, 35B-A3B sparse, 122B-A10B, and 397B-A17B models.

2026-02-13

Qwen3.5 announced as refresh with native multimodality, covered in AI News.

2026-02-15

Qwen3.5-Plus hosted version launched on Alibaba Cloud with 1M context.

2026-02-26

YouTube comparisons of local Qwen3.5 models (397B, 122B, 35B, 27B) published.

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

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