Unverified70% confidenceFactTime unknown
When fine-tuning a Llama 7B model on an RTX 4090 using QLoRA, VRAM usage is approximately 5–8GB.
2
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Unsloth教程：本地微调大模型速度提升数倍，显存节省60%
githubunslothai
Related Entities
Related Claims
Unverified8GB of VRAM (such as the RTX 3060 Ti or RTX 4060) can run a Q4-quantized 7B model, but the experience is acceptable only with context kept within 4K tokens; beyond 8K it easily hits the VRAM limit.79% similarVerifiedQLoRA introduces 4-bit quantization technology, making it possible to fine-tune models with 65 billion parameters on a single RTX 3090/4090 GPU with 24 GB of VRAM.79% similarUnverifiedFine-tuning a 70B model requires about 50 GB of VRAM for QLoRA, about 150 GB for LoRA, and up to 1.1 TB for full fine-tuning with the AdamW optimizer.76% similarPartially VerifiedTaking the 65B-parameter LLaMA model as an example, traditional full fine-tuning requires more than 780GB of VRAM, whereas QLoRA requires only approximately 48GB.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/31620API
curl https://kongchang.com/api/v1/knowledge/claims/31620MCP
get_claim(id=31620)