Mixture of Experts (MoEs) in Transformers

Mixture of Experts (MoEs) in Transformers Mixture of Experts (MoEs) in Transformers Published February 26, 2026 Update on GitHub Upvote 179 Aritra Roy Gosthipaty ariG23498 Pedro Cuenca pcuenq merve merve Ilyas Moutawwakil IlyasMoutawwakil Arthur Zucker ArthurZ Sergio Paniego sergiopaniego Pablo Montalvo Molbap Introduction Over the past few years, scaling dense language models has driven most progress in LLMs. From early models like the original ULMFiT (~30M parameters) or GPT-2 (1.5B parameters, which at the time was considered "too dangerous to release" 🧌), and eventually to today’s hundred-billion–parameter systems, the recipe was simple: More data + more parameters gives better performance.

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• Mixture of Experts (MoEs) in Transformers Published February 26, 2026 Update on GitHub Upvote 179 Aritra Roy Gosthipaty ariG23498 Pedro Cuenca pcuenq merve merve Ilyas Moutawwakil IlyasMoutawwakil Arthur Zucker ArthurZ Sergio Paniego sergiopaniego Pablo Montalvo Molbap Introduction Over the past few years, scaling dense language models has driven most progress in LLMs.
• From early models like the original ULMFiT (~30M parameters) or GPT-2 (1.5B parameters, which at the time was considered "too dangerous to release" 🧌), and eventually to today’s hundred-billion–parameter systems, the recipe was simple: More data + more parameters gives better performance.
• Scaling laws reinforced this trend, but dense scaling has practical limits: Training becomes increasingly expensive.
• Deployment requires significant memory and hardware.
• This is where Mixture of Experts (MoEs) enter the picture.
• If you're already familiar with MoEs and want to jump straight into the engineering work done in transformers, you can head directly to Transformers and MoEs .