Colab is cozy. But production won’t fit on a single GPU.
Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he joins me to demystify distributed training and inference — not just for research labs, but for any ML engineer trying to ship real software.

We talk through:
• From Colab to clusters: why scaling isn’t just about training massive models, but serving agents, handling load, and speeding up iteration
• Zero-to-two GPUs: how to get started without Kubernetes, Slurm, or a PhD in networking
• Scaling tradeoffs: when to care about interconnects, which infra bottlenecks actually matter, and how to avoid chasing performance ghosts
• The GPU middle class: strategies for training and serving on a shoestring, with just a few cards or modest credits
• Local experiments, global impact: why learning distributed systems—even just a little—can set you apart as an engineer

If you’ve ever stared at a Hugging Face training script and wondered how to run it on something more than your laptop: this one’s for you.

LINKS

🎓 Learn more:

Hugo's course: Building LLM Applications for Data Scientists and Software Engineers — https://maven.com/s/course/d56067f338
Zach's course (45% off for VG listeners!): Scratch to Scale: Large-Scale Training in the Modern World -- https://maven.com/walk-with-code/scratch-to-scale?promoCode=hugo39

📺 Watch the video version on YouTube: YouTube link

LM Engineering

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