Unverified50% confidenceFactExact time
Traditional LLM inference frameworks pre-allocate contiguous GPU memory for each request, leading to memory fragmentation, with actual GPU utilization often falling below 50%.
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9/22/2026
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UnverifiedIn traditional LLM inference, the actual utilization rate of the KV cache is often less than 50%, while PagedAttention can raise it to nearly 100%.76% similarUnverifiedIn traditional LLM inference, the approach of pre-allocating contiguous GPU memory for the KV cache of each request often results in actual GPU memory utilization of less than 50%.75% similarVerifiedDuring the autoregressive decoding phase, GPU compute utilization is often less than 10%, making it a memory-intensive operation.74% similarUnverified每个中间推理token与输出token消耗等量的GPU算力，Extra High档位单次请求FLOPs可能是普通生成模式的数十倍74% similarUnverified单条推理通常只能占用GPU SM利用率的5%～15%，而合理的批次大小可将利用率提升至60%～90%71% similar
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