🛠️ Struggling to scale GPU workloads without burning cash?
One of the hardest parts of AI/ML development is getting GPU provisioning just right – too little and performance suffers, too much and budgets spiral out of control.

In this clip, @charles_irl demos how to:

🚀 Spin up GPU instances on demand – no idle resources draining your budget.

📈 Scale effortlessly by adjusting replicas to handle load spikes.

⚙️ Optimize GPU resources to handle fine-tuning and inference workloads without unnecessary overhead.

💸 Avoid idle GPU costs by spinning up instances only when needed and scaling efficiently.

This is a great example of how tools like @modal_labs can simplify GPU scaling – but the real takeaway is understanding how to manage GPU resources efficiently, regardless of platform.

🎧 Listen to/Watch the full episode here for a deeper dive: