Unverified50% confidenceFactExact time
Fine-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.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
Partially 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.80% similarUnverifiedFor a model of approximately 160 GB (such as the UD-Q3_K_XL quantized version of Qwen3.5-397B), running the lens requires approximately 20 GB of additional memory.77% similarUnverifiedFull fine-tuning of a 70B model with the AdamW optimizer requires about 1,129 GB of VRAM (70.55 billion parameters × 16 bytes per parameter).76% similarUnverifiedFine-tuning gpt-oss-20b with QLoRA requires approximately 14 GB of VRAM, and Unsloth's official recommendation is at least 16 GB.76% similarUnverifiedWhen fine-tuning a Llama 7B model on an RTX 4090 using QLoRA, VRAM usage is approximately 5–8GB.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1019924API
curl https://kongchang.com/api/v1/knowledge/claims/1019924MCP
get_claim(id=1019924)