Gemma 4 26B MoE underrated for RAG and personal assistants on a single 3090
Why Qwen3.6 dominates recommendations while Gemma4 26B quietly outperforms for non-coding tasks...
A Reddit user running a solo 3090 with a few side GPUs is building a Jarvis-style RAG and personal assistant system. They note that the common answer for most questions is Qwen3.6 27b or 35b, followed by Gemma4 31b (which doesn’t fit well on a solo 3090). The user is testing Gemma 4 26b MoE for non-coding tasks (RAG, personal assistant, knowledge base queries) and says they think it may possibly be better than Qwen3.6 35b for these use cases. However, they flag that they don’t see many people talking about it anymore and ask what skeletons it has. They plan to keep Qwen for coding.
- User finds Gemma 4 26B MoE outperforms Qwen3.6 35B for RAG and personal assistant tasks on a single 3090
- Qwen3.6 remains dominant in community recommendations, especially for coding, but may be overkill for non-coding AI workflows
- Gemma 4 26B's Mixture of Experts architecture requires less VRAM, making it a better fit for 24GB GPUs than dense 35B models
Why It Matters
Herd mentality in open-source AI can blind users to better-performing models for specific hardware and tasks.