Heuristic:Allenai Open instruct GPU Memory Utilization
| Knowledge Sources | |
|---|---|
| Domains | Optimization, Inference |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Use 90% GPU memory utilization as the default for vLLM inference to prevent OOM while maximizing throughput.
Description
vLLM pre-allocates GPU memory at initialization time based on the `gpu_memory_utilization` parameter. Setting this too high (e.g., 0.95-1.0) causes OOM errors during memory spikes from batch processing. Setting it too low wastes GPU capacity. The 90% default provides a good balance between throughput and stability.
Usage
Apply this heuristic when configuring vLLM for GRPO generation. Reduce to 0.7-0.8 if experiencing OOM errors with very large models or when GPU memory is shared with other processes.
The Insight (Rule of Thumb)
- Action: Set `vllm_gpu_memory_utilization = 0.9` in the data loader configuration.
- Value: 0.9 (90% of available GPU memory).
- Trade-off: 10% memory headroom is wasted but prevents allocation failures during peak usage.
Reasoning
vLLM uses a block-based memory manager that pre-allocates a fixed pool at startup. During inference, temporary tensors (attention scores, intermediate activations) require additional memory beyond the pool. The 10% headroom accommodates these spikes without requiring dynamic reallocation, which would degrade throughput.
Code Evidence
Default value from `open_instruct/data_loader.py:285`:
vllm_gpu_memory_utilization: float = 0.9