Unverified50% confidenceTradeoffExact time
配备高端GPU（如Nvidia A100或H100）的服务器一次性投入后可承载数亿次本地推理，通常在6-18个月内即可回收硬件投资
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/21/2026
First Seen
Valid until: 10/19/2026
Sources
Related Claims
UnverifiedA single inference at the million-token level requires tens to hundreds of gigabytes of high-end GPU memory (A100/H100 class).72% similarUnverifiedGPU上下文切换需保存完整GPU状态（包括寄存器文件、共享内存和L1缓存），数据量可达数百MB，而CPU上下文切换仅需保存几KB71% similarUnverified随着消费级GPU性能提升，在个人电脑上运行70亿至140亿参数量级的模型已具备实用性69% 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69% similarUnverifiedThe GPU multi-level memory hierarchy from highest to lowest is: register file (latency ~1 cycle), shared memory/L1 cache (~32–228 KB, latency ~20–40 cycles), L2 cache (H100 ~50 MB, latency ~200 cycles), and HBM global memory (latency ~500–800 cycles).68% similar
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