How much VRAM does Llama 3.1 8B need to fine-tune at FP16? Adam optimizer math
Full fine-tuning Llama 3.1 8B at FP16 with native 128K context needs about 167.8 GB of VRAM, demonstrating the massive scaling of Adam and activations at high context.
Calculator
Estimated VRAM required
41.1 GB
8B params at FP16, 131,072 token context, batch 1, training (Adam).
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
LoRA fine-tune sizing: Forward weights at FP16, only ~1% of params get optimizer state (FP32 master + grad + AdamW m + v). Real LoRA peak depends on rank and target modules; this is the typical r=16 ceiling.
Hardware that fits
Just barely too small
How this is calculated
Training weights + gradients + Adam optimizer buffers take 144 GB. The 128K KV cache takes 17.2 GB, and activation overhead adds roughly 6.6 GB, totaling 167.8 GB.
Verdict
Full fine-tuning at FP16 is the worst-case memory configuration. Use it only if you must. LoRA reduces memory to roughly inference + a few GB; QLoRA reduces it further by quantizing the base model weights. For 99% of fine-tuning use cases, QLoRA on a single 24 GB GPU produces results indistinguishable from full fine-tuning.
Frequently asked questions
Why does training need so much more memory than inference?
Can I fine-tune Llama 3.1 8B on a single 24 GB GPU?
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