Full Process Solution for Graphics Memory Management
Systematic troubleshooting is required for OOM issues:
| point | prescription |
|---|---|
| When the model is loaded | increase--reserve-gpu-mem 4GBPreservation of buffer space |
| The reasoning process | set upmax_seq_len=2048Limit Context Window |
| long term | start using--enable-mem-poolMemory Pooling Technology |
Key Diagnostic Steps:
- utilization
nvidia-smi -l 1Monitor graphics memory fluctuation patterns - Added at SGLang startup
--verboseParameter outputs a detailed memory allocation log - Recommended for long texts over 4KFlashAttentionsparse attention pattern
Advanced programs may be considered forTensorRT-LLMPerform a model recompile for an additional 20% video memory optimization.
This answer comes from the articleGrok-2: xAI's Open Source Hybrid Expert Large Language ModelThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of Overflow of Memory (OOM) when Grok-2 is deployed locally?
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