Partially Verified80% confidenceBenchmarkExact time
Taking the 65B-parameter LLaMA model as an example, traditional full fine-tuning requires more than 780GB of VRAM, whereas QLoRA requires only approximately 48GB.
4
Sources
80%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
Related Claims
UnverifiedFine-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.80% 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-tuning79% similarUnverifiedLLaMA-2 70B 总参数约 700 亿均匀分布于 80 层，每层约 8.75 亿参数，FP16 精度下单层约占 1.75GB 显存77% similar
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