{"slug": "how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3", "title": "How much VRAM and how many GPUs to fine-tune a 70B parameter model like LLaMA 3.1 locally?", "summary": "A developer created a VRAM calculator for fine-tuning 70B parameter models like LLaMA 3.1 locally, showing that full fine-tuning requires about 1.1 TB of VRAM (8× H100/A100-80GB), while QLoRA can run on a single 48 GB card. The tool covers full fine-tuning, LoRA, and QLoRA methods with adjustable quantization and context length.", "body_md": "Rough VRAM math for fine-tuning a 70B, by method:\n\n**Full fine-tune** (AdamW, mixed precision): ~16 bytes/param once you count fp16 weights + fp32 master weights + gradients + optimizer moments — that’s ~1.1 TB before activations, so realistically 8× H100/A100-80GB with ZeRO-3/FSDP sharding and activation checkpointing, or fewer GPUs with optimizer offload and a lot of patience. Not a home setup.\n\n**LoRA** (fp16 base): the frozen base still needs its ~140 GB resident, plus a small adapter + optimizer state 2× 80 GB, or 4× 48 GB with sharding.\n\n**QLoRA** (4-bit NF4 base): ~40–45 GB for the quantized base + adapter state + activations. A single 48 GB card (A6000/RTX PRO 6000) works at moderate sequence lengths; context length is what pushes it over, since KV/activation memory scales with tokens.\n\nI built a calculator that does this per method/quant/context and shows the formula and every assumption (it covers full/LoRA/QLoRA, not just inference): [https://vram.rxdt.dev](https://vram.rxdt.dev) corrections welcome if your real runs disagree.", "url": "https://wpnews.pro/news/how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3", "canonical_source": "https://discuss.huggingface.co/t/how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3-1-locally/150882#post_3", "published_at": "2026-07-15 21:07:40+00:00", "updated_at": "2026-07-15 21:11:38.638932+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-tools", "ai-research"], "entities": ["LLaMA 3.1", "H100", "A100-80GB", "A6000", "RTX PRO 6000"], "alternates": {"html": "https://wpnews.pro/news/how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3", "markdown": "https://wpnews.pro/news/how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3.md", "text": "https://wpnews.pro/news/how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3.txt", "jsonld": "https://wpnews.pro/news/how-much-vram-and-how-many-gpus-to-fine-tune-a-70b-parameter-model-like-llama-3.jsonld"}}