Dense or MoE for Qwen 3.5 Local Runs?

🔑 Enhanced Key Takeaways

• •Qwen3.5-27B employs a dense architecture with 64 layers, hidden dimension of 5120, 24 attention heads (4 KV heads), RMS normalization, and SwigLU activation, activating all 27B parameters per forward pass[1].
• •Qwen3.5-27B achieves top benchmarks including MMLU-Pro 86.1%, GPQA Diamond 85.5%, SWE-bench Verified 72.4%, and supports 262k native context extensible to 1M across 201 languages[1][4].
• •Dense Qwen3.5-27B outperforms MoE variant Qwen3.5-35B-A3B in complex coding and reasoning (e.g., 7.5 t/s vs 46 t/s on similar hardware, but superior logic depth)[3][5].
• •On 24GB VRAM GPUs like RTX 4090, Qwen3.5-27B runs at Q6/Q8 quantization for high quality, requiring ~16-18GB at Q4[3].

🛠️ Technical Deep Dive

• •Architecture: Dense model with Gated Delta Networks (GDN) and Feed Forward Networks; hidden layout 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))[2].
• •Attention: Grouped-Query Attention with 24 Q-heads (head dim 256), 4 KV-heads (head dim 128), Rotary Position Embedding (ROPE) dim 64[1][2].
• •FFN: Intermediate dimension 17408, SwigLU activation, RMS Normalization[1][2].
• •Multimodal: Unified vision-language capabilities, supports 201 languages, 262k context (ext. 1M)[1].
• •Quantization: Runs on consumer GPUs; Q4 needs ~16GB VRAM, Q6/Q8 on 24GB GPUs[3].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline