Unverified50% confidenceBenchmarkExact time
Under equivalent hardware conditions, vLLM can achieve several times the throughput improvement compared to a naive inference implementation.
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Long-term
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9/29/2026
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UnverifiedDatabricks can leverage high-performance inference engines such as vLLM or TensorRT-LLM to achieve 3–5 times higher throughput on the same hardware compared to standard deployments.79% similarUnverifiedvLLM结合连续批处理策略，在相同硬件上的吞吐量较Hugging Face原生实现提升可达24倍77% similarUnverifiedvLLM 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.71% similarVerifiedModern inference frameworks such as vLLM and TensorRT-LLM improve GPU utilization through techniques such as Continuous Batching and PagedAttention.70% similarUnverifiedUnder equivalent hardware conditions, vLLM's throughput is typically several times to more than ten times higher than native HuggingFace Transformers inference.70% similar
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