Verified65% confidenceBenchmarkExact time
A 140 GB FP16 70B model can be compressed to approximately 35–40 GB after 4-bit quantization, reducing communication overhead by about 75%; 2-bit quantization can compress it to approximately 18 GB but introduces accuracy loss.
3
Sources
65%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Mesh LLM: A Practical Exploration of Building a P2P Distributed AI Inference Network with iroh
hackernewshackernews7/11/2026
Related Claims
VerifiedA 70B-parameter FP16 model requires approximately 140 GB of video memory, approximately 70 GB after INT8 quantization, and approximately 35–40 GB after INT4 quantization.82% similarVerifiedA 70B model can be compressed to approximately 35GB at INT4 precision.81% similarVerifiedEven compressing the weights to 214GB using 4-bit quantization still far exceeds the capacity of a single 48GB GPU.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% similarVerifiedA 35B model requires approximately 70GB of VRAM at FP16 precision, far exceeding the RTX 5090's 32GB capacity, so single-GPU deployment requires quantization.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)