Unverified50% confidenceFactExact time
GPU memory bandwidth growth has long lagged behind compute growth, causing the actual time of operations like attention to be determined by data movement between HBM and on-chip SRAM rather than pure computation volume.
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50%
Confidence
Long-term
Relevance
10/26/2026
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UnverifiedGPU HBM has the lowest access latency but limited capacity and the highest cost; CPU DRAM has larger capacity but access requires passing through the PCIe bus with significantly increased latency; the bandwidth gap between different tiers often reaches more than an order of magnitude.78% similarUnverifiedAuto-scaling of GPU instances is more complex than for ordinary web services because model loading is slow and GPU resources are scarce and expensive.74% similarVerifiedFor modern CPUs and GPUs, memory bandwidth is often the true bottleneck for inference speed rather than the compute units' arithmetic throughput, which is why quantization can bring speed improvements.74% similarUnverifiedKeeping model weights fully resident in GPU memory avoids memory swap-in/swap-out overhead, whereas models that exceed GPU memory capacity require data to be moved between CPU and GPU, causing latency to rise significantly.74% similarUnverified自回归推理是memory-bound（内存带宽受限）问题，因为batch size通常为1时算术强度极低，GPU大部分时间在等待数据搬运74% similar
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