REDDIT · REDDIT// 4h agoBENCHMARK RESULT

Gemma 4, Qwen 3.5 energy-performance tradeoffs reveal MoE lead

Independent benchmarking of the 30B-class Gemma 4 and Qwen 3.5 models reveals a stark energy-performance divide between Mixture-of-Experts (MoE) and dense architectures. While both families achieve high accuracy on complex reasoning tasks, MoE variants provide a significantly better "intelligence per watt" ratio, often outperforming dense counterparts with nearly 4x less energy consumption per trial.

// ANALYSIS

MoE architectures have cemented their status as the only viable path for efficient local inference at the 30B scale.

–Gemma 4 26B (MoE) delivered the most efficient correct answers, using just 1.9Wh compared to 7.07Wh for the dense Qwen 3.5 27B on the same task.
–The energy penalty for dense models is massive; Qwen 3.5 27B consumed over 3x the power of its MoE counterparts for identical output quality.
–Perplexity serves as a critical "canary in the coal mine," as Qwen 3.5 35B's slight logprob dip correctly signaled its failure on non-standard framing.
–Tracking "thinking characters" and energy draw highlights that increased compute does not linearly translate to better decision quality in local deployment.

// TAGS

llmbenchmarkopen-weightsenergy-efficiencygemma-4qwen-3-5moe

DISCOVERED

4h ago

2026-04-21

PUBLISHED

4h ago

2026-04-21

RELEVANCE

8/ 10

AUTHOR

gigDriversResearch