Unverified50% confidenceBenchmarkExact time
上下文拉长后,20.7B稠密模型约两成参数溢出到内存,速度暴跌至每秒5.7个Token,而矮星几乎不受影响
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/25/2026
First Seen
Valid until: 10/23/2026
Sources
Redis之父如何将2840亿参数DeepSeek塞进普通笔记本
Related Claims
UnverifiedThe model weights alone of a 175-billion-parameter model (in BF16 precision) require 350 GB of storage, and the Adam optimizer must maintain two sets of states—first-order moments and second-order moments—totaling more than 1 TB.70% similarUnverifiedMeta's LLaMA 2 70B model still requires approximately 35 GB of VRAM to fully load even with 4-bit quantization.69% similarVerified4-bit量化理论上可将模型体积缩小8倍(相比32位存储)67% similarUnverifiedQuantprobe claims to enable a machine with only 6GB of VRAM and 16GB of RAM to run 30B-class models at 22 tokens/s.67% similarVerifiedThe speed bottleneck in large model inference is memory bandwidth rather than insufficient computing power, as generating each token requires loading hundreds of GB of model weights from memory.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/613180API
curl https://kongchang.com/api/v1/knowledge/claims/613180MCP
get_claim(id=613180)