AI2026-05-17
Dense Model
Also known as: Dense Model / Dense Transformer / 高密度モデル
A standard Transformer model where all parameters participate in processing every token, as opposed to MoE's sparse expert selection. Compute scales proportionally with parameter count.
Overview
A Dense Model is a standard Transformer where every parameter activates for every input token. Llama, Qwen Dense, Gemma, and Phi are examples. The term is used in contrast to MoE: compute scales linearly with parameter count, so scaling up requires proportionally more compute.
When to choose Dense vs MoE
Dense models require less VRAM than MoE at the same parameter count and are simpler to deploy. 7B-27B dense models run efficiently on consumer GPUs and are the dominant choice for local LLM use cases.
Related Columns
AI
Qwen 3.5 27B Dense & 35B-A3B MoE Complete Guide — DFlash Acceleration Breaks 24GB GPU Limits [2026]
Compare Qwen 3.5 27B Dense vs 35B-A3B MoE, check 24GB GPU requirements, learn DFlash 2–3x acceleration, and follow step-by-step Ollama setup instructions.
AI
Local LLM Landscape April 2026 — Top 10 Open-Source Models Comprehensive Comparison [Ollama Guide]
Comprehensive comparison of the top 10 local LLMs as of April 2026. Covers SWE-bench scores, Japanese language performance, VRAM requirements, Ollama commands, and licensing for Gemma 4, Llama 4, Qwen 3.5, GLM-5.1, Kimi K2.5, MiniMax M2.5, and more.
AI
Gemma 4 System Requirements — 5–62GB VRAM, RTX 3060 to H100 by Variant (E2B/E4B/26B/31B) [2026 Guide]
Gemma 4 needs 5GB VRAM (E2B/E4B), 16GB (26B MoE), or 24-62GB (31B Dense) depending on quantization. Requirements by model: RTX 3060 to H100, Apple Silicon M1-M4, CPU-only operation, RAM sizing, and budget builds. Updated July 2026.
Related Terms
Feel free to contact us
Contact Us