Unverified85% confidenceFactTime unknown
Kimi K2在FP16精度下仅模型权重就需要数百GB显存，INT4量化可将显存需求压缩至约四分之一
1
Sources
85%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
Verified4-bit量化（如Q4_K_M）通常可将3B模型内存占用从约6GB压缩至约2GB，精度损失低于2-3%75% similarUnverifiedThe 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.73% similarUnverifiedK-Quant动态量化在极端配置下能把Muse Glimmer显存需求压到3GB级别，模型质量损失仅约0.2%73% similarVerifiedIn FP16 precision, large models require approximately 2 GB of VRAM per 1 billion parameters.73% similarUnverifiedA model with 70 billion parameters that originally requires 80 GB of VRAM requires only about 40 GB of memory after 4-bit quantization (Q4_K_M level), with inference quality loss typically within 3%–8% on most benchmarks.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/29545API
curl https://kongchang.com/api/v1/knowledge/claims/29545MCP
get_claim(id=29545)