MOE vs Dense: Which Architecture Will Drive the Next Wave of LLMs?

MOE vs Dense is no mere buzzword clash. In a hot thread, fans map scalability, efficiency, OSS, and vision LLMs against real VRAM limits, and the verdict isn’t simple ^[1].

On speed and scale, MOE models go fast—fast enough to feel like smaller dense models while scaling smarter at the same speed ^[1]. Dense models still win in some niches; as one commenter puts it, "Dense 12b consistently punches above its weight class though" ^[1].

OSS and vision LLMs add fuel to the fire. Here’s what’s catching eyes:

• Gemma and its MOE line are in the mix, with hopes for Gemma 4 carrying 30B MOE—aiming to match what Qwen offerings do in a similar size ^[1]. • Qwen3-VL represents OSS vision capabilities pushing into larger stacks ^[1]. • Magistral 24B’s vision chops are called top notch in discussions about next-gen vision LLMs ^[1]. • GPT-OSS-20B surfaces as one of the few models considered worth using around that size ^[1].

Practical constraints aren’t slowing the debate either:

• There’s talk of more MOE models in 15-30B range designed for 8GB VRAM ^[1]. • The VRAM floor is a real limiter, nudging the field toward architecture choices that fit consumer GPUs while still delivering scale ^[1].

Closing thought: the next wave will likely blend MOE scalability with dense efficiency, as Gemma variants and the Qwen family push what’s possible in 2025 and beyond ^[1].

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