This blog explains a systematic way to fix CUDA out-of-memory (OOM) errors during GRPO reinforcement learning training, instead of randomly lowering hyperparameters until something works. Subham argues that most GPU memory issues come from three sources: vLLM reserving GPU memory upfront (often the biggest chunk), training activations (which scale with batch size, sequence length, number of generations, and model size), and model memory (usually the smallest contributor). By carefully reading the OOM error message and estimating how memory is distributed across these components, you can identify exactly what’s causing the crash. The recommended approach is to calculate memory usage first, then adjust the highest-impact settings, such as GPU memory allocation for vLLM, number of generations, batch size, and sequence length. The guide also shows how to maintain training quality by using techniques like gradient accumulation instead of simply shrinking everything. Overall, the key message is: treat OOM debugging as a measurable engineering problem, not trial-and-error, so you can fix memory issues faster while preserving training performance.

Enterprise organizations face a critical paradox in AI deployment: while 52% struggle to access needed GPU resources with 6-12 month waitlists, 83% of existing CPU capacity sits idle. This talk introduces an approach to AI infrastructure optimization through universal resource management that reshapes applications to run efficiently on any available hardware—CPUs, GPUs, or accelerators. We explore how code reshaping technology can unlock the untapped potential of enterprise computing infrastructure, enabling organizations to serve 2-3x more workloads while dramatically reducing dependency on scarce GPU resources. The presentation demonstrates why CPUs often outperform GPUs for memory-intensive AI workloads, offering superior cost-effectiveness and immediate availability without architectural complexity.