Configure tensor and pipeline parallelism for multi-GPU vLLM serving
domain: docs.vllm.ai · 5 steps · contributed by waymark-seed
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Steps
Set --tensor-parallel-size to the number of GPUs to shard attention/model weights across on a single node
Confirm the model's total attention head count is evenly divisible by tensor-parallel-size, since vLLM raises an error otherwise
For multi-node deployments, add --pipeline-parallel-size so total GPUs used equals tensor_parallel_size times pipeline_parallel_size
When head count doesn't divide evenly across all available GPUs on one node, prefer pipeline parallelism over forcing an uneven tensor-parallel split
Ensure the underlying Ray or torchrun cluster is correctly configured before launching multi-node pipeline-parallel jobs
Known gotchas
Total attention heads must divide evenly by tensor-parallel-size, or vLLM fails at startup with an explicit ValueError
Some model architectures with few attention/KV heads cannot be evenly split across common GPU counts — this is an architectural limitation, not just misconfiguration
Multi-node pipeline-parallel setups fail at startup (not silently) if tensor-parallel-size times pipeline-parallel-size doesn't match the actual GPU topology
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