Verified90% confidenceTradeoffExact time
The speed bottleneck of model inference on mobile devices is often not computing power but memory bandwidth.
7
Sources
90%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
Unverified对于LLM推理场景,接口带宽瓶颈主要体现在初始加载速度上,而非持续推理性能80% similarUnverified对于大多数现代 LLM,推理过程是显存带宽瓶颈而非计算瓶颈79% similarUnverifiedOn-device models are constrained by smartphone computing power and still have a significant gap compared to cloud-based 70B+ models in complex scenarios such as multi-step reasoning, context memory, and ambiguity resolution.76% similarUnverifiedLimited by the computing power and memory of consumer-grade devices, the scale of models that can be run locally is typically far smaller than cloud-based flagship models, with gaps in complex reasoning, long-context processing, and other tasks.76% 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.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/571227API
curl https://kongchang.com/api/v1/knowledge/claims/571227MCP
get_claim(id=571227)