Expired50% confidenceFactExact time
Ollama uses quantization schemes such as Q4_K_M by default, reducing the VRAM requirement for a 31B-parameter model from approximately 62GB (FP16) to approximately 20GB.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Gemma 4 Open-Source Model Local Deployment Guide: Ollama Installation & Mobile Setup
bilibili搞机零距离-6/10/2026
Related Claims
Unverifiedllama.cpp项目使27B参数模型的显存需求从FP16约54GB降至Q4量化约16GB80% similarPartially VerifiedThe QLoRA paper verified that NF4 quantization combined with LoRA fine-tuning can reduce memory requirements from 780GB to 48GB on a 65B parameter model while maintaining performance close to full-precision fine-tuning77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/47987API
curl https://kongchang.com/api/v1/knowledge/claims/47987MCP
get_claim(id=47987)