Verified65% confidenceFactTime unknown
During the autoregressive decoding phase, GPU compute utilization is often less than 10%, making it a memory-intensive operation.
3
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65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
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Related Claims
VerifiedDuring large model inference, actual GPU utilization is often less than 10%.85% similarVerifiedThe price of output tokens is typically 4–6 times that of input tokens, stemming from the computational differences between the prefill and decode phases, with GPU utilization during the decode phase potentially being only 10–20% of that during prefill77% similarUnverifiedPure CPU inference is typically 5–10 times slower than GPU inference.76% similarUnverifiedTraditional LLM inference frameworks pre-allocate contiguous GPU memory for each request, leading to memory fragmentation, with actual GPU utilization often falling below 50%.74% similarUnverifiedFreeToken supports runtime adjustment of the GPU expert cache size; tests show that reducing the cache from 58% to 40% results in only about 10% performance loss, with performance dropping sharply below 20%.74% similar
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