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// 45d 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
45d ago
2026-04-21
PUBLISHED
45d ago
2026-04-21
RELEVANCE
8/ 10
AUTHOR
gigDriversResearch