Verified75% confidenceFactExact time
The LLM decoding process is memory bound, with the GPU spending most of its time waiting for data transfers during token-by-token generation.
3
Sources
75%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
UnverifiedMemory bandwidth, rather than compute (FLOPS), is the core bottleneck determining local LLM inference speed, because generating each token requires reading nearly all model weights from memory into the compute units76% similarUnverifiedTexture assets, even after compression, still need to be decoded into memory at runtime for GPU use, and decoding occupies main-thread time and delays the first render.75% similarUnverifiedThe Prefill stage of LLM inference is compute-intensive with GPU utilization close to 100%, while the Decode stage is memory-bandwidth-intensive with the bottleneck being HBM VRAM bandwidth.74% similarUnverified传统GPU如NVIDIA A100/H100在推理阶段面临内存带宽瓶颈，模型权重需要在每次生成Token时从显存反复读取73% similarUnverifiedThe LLM output stage (decode) is limited by the serial nature of autoregression, resulting in lower GPU utilization than the batch parallel processing of the input stage (prefill), which causes the unit price per output token to typically be the highest73% similar
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