Intro to Mixture of Experts | Aritra Roy Gosthipaty | HF Podcast #2

In this episode, Alejandro sits down with Aritra Roy Gosthipaty from the Hugging Face Transformers team to talk about mixture-of-experts models, why dense models still matter, how synthetic data changes training, and what coding agents are changing for working engineers. They discuss Mixtral, DeepSeek-V2, Switch Transformers, vLLM, Inference Providers, TinyAya, data curation, local inference limits, and how practitioners should think about coding with agents without losing core engineering skill. More from Aritra: - Hugging Face profile — https://huggingface.co/ariG23498 Chapters 00:00 Meet Aritra Roy Gosthipaty 00:45 How Aritra joined Hugging Face 03:05 What a Developer Advocate on Transformers works on 04:00 What mixture-of-experts models are 08:26 Why MOEs matter now 11:36 Where dense models still win 15:00 Where to start learning and training MOEs 18:44 Synthetic data, data engines, and data quality 22:18 Why MOEs are still hard to run locally 23:11 How coding tools changed engineering work 25:49 Do agents weaken creativity and skill? 28:29 Should beginners rely on coding agents? 33:21 What coding will look like in a year 35:25 The biggest recent AI wow moments If you enjoyed the episode, subscribe for more conversations about open models, infrastructure, and the future of AI. --- Sources / References • Aritra Roy Gosthipaty — https://huggingface.co/ariG23498 • Mixture of Experts Explained — https://huggingface.co/blog/moe • Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer — https://arxiv.org/abs/1701.06538 • vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention — https://arxiv.org/abs/2309.06180 • DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model — https://arxiv.org/abs/2405.04434 • Mixtral of Experts — https://arxiv.org/abs/2401.04088 • Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity — https://arxiv.org/abs/2101.03961 • Hugging Face Inference Providers — https://huggingface.co/docs/inference-providers/index • Unsloth Docs — https://unsloth.ai/docs • MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases — https://arxiv.org/abs/2402.14905 • Tiny Aya: Bridging Scale and Multilingual Depth — https://arxiv.org/abs/2603.11510 • Aya 23: Open Weight Releases to Further Multilingual Progress — https://arxiv.org/abs/2405.15032 • Self-Instruct: Aligning Language Models with Self-Generated Instructions — https://arxiv.org/abs/2212.10560 • The Curse of Recursion: Training on Generated Data Makes Models Forget — https://arxiv.org/abs/2305.17493 • DeepSeek-V3 Technical Report — https://arxiv.org/abs/2412.19437