[KongchangAI]
Unverified60% confidenceFactExact time

Q3_K_M compresses each weight to approximately 3 bits, reducing the weight size of a 27B model to approximately 12–13 GB.

2
Sources
60%
Confidence
Long-term
Relevance
7/11/2026
First Seen

Sources

Why Is Qwen3 27B So Slow on a 4090? A Hands-On Guide to Local Inference Performance Optimization
redditr/ollama7/10/2026

Related Claims

UnverifiedSwitching to Q2K quantization reduces a single expert from 3.1MB to 1.9MB, and the full weights from 111GB to 75GB.78% similarUnverifiedM3原始权重存储下限：BF16约854GB，8-bit约428GB，4-bit约214GB76% similar

Cite This Claim

Stable URI
https://kongchang.com/claim/486087
API
curl https://kongchang.com/api/v1/knowledge/claims/486087
MCP
get_claim(id=486087)