Unverified50% confidenceFactExact time
vLLM configuration parameters include --tensor-parallel-size (tensor parallelism degree), --gpu-memory-utilization (GPU memory usage ratio cap), --max-num-batched-tokens (maximum number of batched tokens), and others.
1
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50%
Confidence
Long-term
Relevance
9/22/2026
First Seen
Sources
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Related Claims
UnverifiedThe parallelism setting in vLLM must match the actual number of GPUs; setting GPU memory utilization too high causes OOM crashes, while setting it too low wastes hardware resources.75% similarVerifiedModern inference frameworks such as vLLM and TensorRT-LLM improve GPU utilization through techniques such as Continuous Batching and PagedAttention.75% similarUnverifiedvLLM 支持多 GPU 分布式推理，主要采用张量并行策略，底层集成 Megatron-LM 的并行算子并利用 NCCL 库实现 GPU 间通信75% similarUnverifiedModels within 33B can be efficiently deployed on a single GPU or a small number of GPUs, which is the range where vLLM's optimization capabilities deliver the most value.74% similarUnverifiedvLLM is primarily optimized for NVIDIA GPUs and offers less support for CPU inference and low-end hardware than llama.cpp.74% similar
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