How much VRAM does Llama 3.1 8B need to fine-tune at FP16? Adam optimizer math
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, and 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.
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.
By TechCompare · Updated
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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 or hybrid state estimates. 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
Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.
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
The 167.8 GB total comes from 144 GB of training weights + gradients + Adam optimizer states (which at FP16 plus an FP32 master plus the two FP32 Adam buffers is roughly 4x the inference weight cost), 17.2 GB of FP16 KV cache at the full 128K context, and 6.6 GB of activation overhead. A single 24 GB consumer card cannot host this configuration, which is the entire point: Adam's two FP32 buffers per parameter triple-or-quadruple per-parameter memory, then activations saved for backprop add another chunk. LoRA drops the optimizer and gradient memory to a few GB by training only adapter weights, and QLoRA goes further by quantizing the base model to 4-bit before training the adapters, fitting a single 24 GB card comfortably with results that match full fine-tuning for most use cases.
More Training scenarios
Related guides
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?
What does QLoRA actually store during fine-tuning?
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