Unverified50% confidenceFactExact time
NCM shifts the inference bottleneck from memory bandwidth to compute, greatly reducing the number of weight reads, but it processes more positions than the number of output tokens, increasing computation.
1
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50%
Confidence
Long-term
Relevance
10/31/2026
First Seen
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Related Claims
UnverifiedThrough hierarchical parallel prediction, NCM can theoretically reduce generation complexity from O(n) to O(log n) or lower, at the cost of potentially losing fine-grained information.74% similarUnverifiedThe bottleneck of on-device inference is often not peak compute but memory bandwidth; ternary quantization reduces the amount of data that needs to be read per inference by compressing parameters, which is key to achieving 120 tok/s.73% similarUnverified在推理阶段内存带宽往往比纯算力更早成为瓶颈，注意力机制需在每步生成时读取全部KV缓存，上下文长度增大时内存读写开销呈平方级增长71% similarUnverifiedProduct quantization converts distance computation from per-dimension floating-point operations into integer table-lookup accumulation using precomputed codebook distances, thereby improving batch retrieval throughput70% similarUnverifiedThe Ngram lookup table can be streamed from SSD without seriously slowing down inference, because the memory bandwidth requirements of the table lookup operation are far lower than those of the matrix operations in neural network layers.70% similar
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