Training big models used to be reserved for OpenAI or DeepMind. But these days? Builders everywhere have access to clusters of 4090s, Modal credits, and open-weight models like LLaMA 3 and Qwen. ​​Zach Mueller, Technical Lead for Accelerate at Hugging Face and creator of a new course on distributed ML, joins us to talk about what scaling actually looks like in 2025 for individual devs and small teams. ​We’ll break down the messy middle between “just use Colab” and “spin up 128 H100s,” and explore how scaling, training, and inference are becoming skills that every ML builder needs. ​We’ll cover: ​⚙️ When (and why) you actually need scale ​🧠 How distributed training works under the hood ​💸 Avoiding wasted compute and long runtimes ​📦 How to serve models that don’t fit on one GPU ​📈 Why this skillset is becoming essential—even for inference ​Whether you’re fine-tuning a model at work, experimenting with open weights at home, or just wondering how the big models get trained, this session will help you navigate the stack—without drowning in systems details.