Worried about GPU memory while fine-tuning?
TL;DR: To fine-tune big models on small GPUs, shrink memory use with these tricks:
- Quantization → smaller base model.
- LoRA → train only small adapter layers.
- 8-bit optimizers → lighter parameter updates.
- Gradient accumulation → fake large batches with small ones.
- Gradient checkpointing → save memory by recomputing activations later.
Mixing these methods makes fine-tuning feasible even on mid-range GPUs.
How to Reduce Memory Usage When Fine-Tuning Large Models
Fine-tuning large language models can quickly hit GPU memory limits, especially on consumer hardware. Luckily, there are several techniques that can help reduce memory usage without sacrificing too much performance. Here are the most commonly used methods:
1. Quantization
Quantization reduces the precision of the model’s weights (for example, from 16-bit to 8-bit). This immediately shrinks the memory footprint of the base model and makes it possible to fit larger models onto smaller GPUs.
2. Low-Rank Adaptation (LoRA)
Instead of updating all the parameters of the model, LoRA adds small trainable matrices (adapters). This makes fine-tuning much more efficient, since only a fraction of parameters are updated. LoRA also reduces optimizer memory requirements.
3. 8-bit Optimizers
Optimizers can also be quantized to 8-bit precision. This further cuts memory use during the parameter update step, though the benefits may be less noticeable if you’re already using gradient accumulation.
4. Gradient Accumulation
This technique allows you to simulate training with a larger batch size by splitting it into smaller chunks. Instead of running one big batch, you process multiple small batches and accumulate the gradients before updating the model. This reduces memory requirements for activations and batches.
5. Gradient Checkpointing
Gradient checkpointing trades computation for memory. Instead of storing all intermediate activations during the forward pass, it saves only a subset. The missing activations are recomputed during backpropagation, reducing peak memory usage at the cost of extra compute.
Where These Techniques Help
Memory usage during training can be thought of in stages:
- Stage 0 – Loading the model: Quantization reduces base model size.
- Stage 1 – Forward pass (batch + activations): Gradient accumulation helps reduce batch memory, while checkpointing and Flash Attention reduce activation memory.
- Stage 2 – Backpropagation: LoRA helps by reducing how many gradients need to be stored.
- Stage 3 – Optimizer step: LoRA, 8-bit optimizers, and gradient accumulation all reduce optimizer memory use.
Practical Tips
- Always quantize the base model and use LoRA adapters.
- Gradient accumulation won’t combine well with 8-bit optimizers—you’ll lose some memory savings.
- If your GPU supports it, use Flash Attention 2 (or PyTorch’s SDPA for newer models).
- If you still run out of memory, gradient checkpointing is often the most reliable fallback.
In short: Combining quantization, LoRA, and memory-efficient optimizers with techniques like gradient checkpointing makes it possible to fine-tune large models even on modest GPUs.