---
title: "Qwen2.5-14B OOM on RTX 3080 Ti (12GB)"
domain: troubleshooting
category: gpu-display
tags: [gpu, vram, oom, qwen, cuda, fine-tuning]
status: published
created: 2026-04-02
updated: 2026-04-02
---
# Qwen2.5-14B OOM on RTX 3080 Ti (12GB)

## Problem

When attempting to run or fine-tune **Qwen2.5-14B** on an NVIDIA RTX 3080 Ti with 12GB of VRAM, the process fails with an Out of Memory (OOM) error:

```
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate X GiB (GPU 0; 12.00 GiB total capacity; Y GiB already allocated; Z GiB free; ...)
```

The 12GB VRAM limit is hit during the initial model load or immediately upon starting the first training step.

## Root Causes

1. **Model Size:** A 14B parameter model in FP16/BF16 requires ~28GB of VRAM just for the weights.
2. **Context Length:** High context lengths (e.g., 4096+) significantly increase VRAM usage during training.
3. **Training Overhead:** Even with QLoRA (4-bit quantization), the overhead of gradients, optimizer states, and activations can exceed 12GB for a 14B model.

---

## Solutions

### 1. Pivot to a 7B Model (Recommended)

For a 12GB GPU, a 7B parameter model (like **Qwen2.5-7B-Instruct**) is the sweet spot. It provides excellent performance while leaving enough VRAM for high context lengths and larger batch sizes.

- **VRAM Usage (7B QLoRA):** ~6-8GB
- **Pros:** Stable, fast, supports long context.
- **Cons:** Slightly lower reasoning capability than 14B.

### 2. Aggressive Quantization

If you MUST run 14B, use 4-bit quantization (GGUF or EXL2) for inference only. Training 14B on 12GB is not reliably possible even with extreme offloading.

```bash
# Example Ollama run (uses 4-bit quantization by default)
ollama run qwen2.5:14b
```

### 3. Training Optimizations (if attempting 14B)

If you have no choice but to try 14B training:
- Set `max_seq_length` to 512 or 1024.
- Use `Unsloth` (it is highly memory-efficient).
- Enable `gradient_checkpointing`.
- Set `per_device_train_batch_size = 1`.

---

## Maintenance

Keep your NVIDIA drivers and CUDA toolkit updated. On Windows (MajorRig), ensure WSL2 has sufficient memory allocation in `.wslconfig`.

---