# Fix Ollama out of memory or model too large

> Ollama ran out of memory or the model is too large. Check the split with ollama ps, then cut the model, the quant, or the context window.

- Canonical: https://guides-ai.com/guides/fix-ollama-out-of-memory/
- Plate 09.17 · Topic: Local LLMs (https://guides-ai.com/topics/local-llm/)
- Published: 21 Oct 2026 · 3 min read
- Source site: guides-ai — https://guides-ai.com/

Ollama has to fit a model in VRAM, or in VRAM plus system RAM. When it cannot, it either splits the model across GPU and CPU, which is slow, or refuses to load it and logs an error such as `model requires more system memory than is available`. The fix is to load less: a smaller model, a lower quantization, or a shorter context.

## 1. Confirm the CPU/GPU split

```bash
ollama ps
```

The `PROCESSOR` column reads `100% GPU` (good), `100% CPU`, or a split like `48%/52% CPU/GPU` when the model spilled out of VRAM.

## 2. Pick a smaller model or lower quantization

See what is installed and how big each build is:

```bash
ollama list
```

The `SIZE` column shows each model on disk; switch to a smaller parameter count, or a tighter quant such as a q4 build instead of q8 or fp16. See [how much RAM or VRAM a model needs](/guides/local-llm-ram-vram-requirements/) and [how to choose a quantization](/guides/choose-llm-quantization/).

## 3. Shrink the context window

A larger context costs memory for the KV cache, so lower it. The default is 4096 tokens.

On macOS or Linux:

```bash
OLLAMA_CONTEXT_LENGTH=4096 ollama serve
```

On Windows (PowerShell):

```powershell
$env:OLLAMA_CONTEXT_LENGTH=4096; ollama serve
```

Each sets the default context for the server. Per model, run `/set parameter num_ctx 4096` inside `ollama run`.

## 4. Free VRAM other apps are holding

```bash
nvidia-smi
```

Lists every process on the GPU; close browsers, games, or a second model so Ollama gets a clean `100% GPU` load.

## Verify it worked

```bash
ollama ps
```

A `100% GPU` in the `PROCESSOR` column means the model fits.

**If it fails:**

- Still `100% CPU` on a good GPU: the GPU was never detected, so fix that first in [Ollama not using your GPU](/guides/ollama-gpu-not-detected/).
- `model requires more system memory`: the model is still too big; read the Windows log at `%LOCALAPPDATA%\Ollama\server.log` (or `~/.ollama/logs/server.log`) for the exact figures, then pick a smaller [quantization](/glossary/#quantization).
- Memory frees slowly after a run: [Ollama](/glossary/#ollama) unloads idle models after 5 minutes; shorten it with `OLLAMA_KEEP_ALIVE`. Full behaviour in the [Ollama FAQ](https://docs.ollama.com/faq).