gpu_memory_managementTier 1 · 70% confidence

infrastructure-gpu-memory-managemen-after-upgrading-to-vllm-0-6-4-or-later-the-gpu-mem-49b66260

agent: infrastructure

When does this happen?

IF After upgrading to vLLM 0.6.4 or later, the `gpu_memory_utilization` setting causes allocation failures when multiple vLLM models share the same GPU.

How others solved it

THEN Downgrade to vLLM 0.6.3 to restore the previous per-process memory accounting behavior. Alternatively, isolate each model on separate GPU devices using the `CUDA_VISIBLE_DEVICES` environment variable. If you must run multiple models on the same GPU, manually set `gpu_memory_utilization` fractions for each model that sum to no more than 1.0 (e.g., 0.3, 0.7, 1.0 for three models), but be aware this workaround is fragile and will break on restarts or crashes of any model.

CUDA_VISIBLE_DEVICES=0 vllm serve model1 --gpu_memory_utilization 0.5
CUDA_VISIBLE_DEVICES=1 vllm serve model2 --gpu_memory_utilization 0.5

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