Unverified60% confidenceBenchmarkExact time
140GB的FP16 70B模型经4-bit量化后可压缩至约35-40GB，通信开销降低约75%；2-bit量化可压缩至约18GB但带来精度损失
2
Sources
60%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Mesh LLM：用iroh构建P2P分布式AI推理网络的实践探索
hackernewshackernews7/11/2026
Related Claims
Verified一个70B参数的FP16模型大约需要140GB显存，INT8量化后约需70GB，INT4量化后约需35-40GB82% similarUnverified70B模型在INT4精度下可压缩至约35GB81% similarUnverified即便用 4-bit 量化把权重压缩到 214GB，仍远超单张 48GB 显卡的容量80% similarVerified32B参数规模的模型在量化后通常可以压缩到约16-20GB显存占用，一张NVIDIA RTX 4090（24GB显存）就能运行80% similarUnverified35B模型在FP16精度下需要约70GB显存，远超RTX 5090的32GB容量，因此单卡部署需量化79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/497386API
curl https://kongchang.com/api/v1/knowledge/claims/497386MCP
get_claim(id=497386)