How to Update Ollama to the Latest Version (All Platforms)

Keeping Ollama updated matters more than it might seem. New versions bring faster inference, support for newly released models, bug fixes for GPU detection issues, and sometimes significant performance improvements for specific hardware. The update process is quick on all platforms — usually under two minutes — and your downloaded models are never touched during an update.

This guide covers how to update Ollama on Windows, macOS, and Linux, what changes between versions, and how to check whether a new version actually fixes something you care about before updating.

How to Check Your Current Version

Before updating, it’s useful to know what version you’re running:

ollama --version

You can also check the Ollama GitHub releases page at github.com/ollama/ollama/releases to see what the latest version is and what changed. The changelog is worth a quick scan — Ollama releases frequently (sometimes weekly) and the notes are specific about what’s fixed or added.

Updating on macOS

The easiest way: click the Ollama icon in your menu bar and look for an “Update Available” option. When a new version is out, Ollama notifies you through the menu bar. Click it, let it download, and it restarts automatically.

If you don’t see the notification or want to force a check, you can update manually by downloading the latest .dmg from ollama.com, running it, and dragging the new version to Applications — overwriting the existing one. Your models and configuration are stored separately in ~/.ollama and aren’t affected.

If you installed via Homebrew:

brew upgrade ollama

Then restart the Ollama service:

brew services restart ollama
# or if you run it manually:
ollama serve

Updating on Linux

On Linux, updating is the same command as the original install — the script detects an existing installation and upgrades in place:

curl -fsSL https://ollama.com/install.sh | sh

The script downloads the new binary, replaces the old one at /usr/local/bin/ollama, and restarts the systemd service automatically. Your models in /usr/share/ollama/.ollama/models (or wherever you’ve pointed OLLAMA_MODELS) are untouched.

Verify the update worked:

ollama --version
systemctl status ollama

If you want to automate updates on a server, you can set up a simple cron job or systemd timer to run the install script periodically. Just be aware that automatic updates without testing can occasionally introduce regressions — for production setups, testing on a non-critical machine first is worth it.

Updating on Windows

Ollama on Windows has an automatic update mechanism built in. When a new version is available, you’ll see a notification in the system tray. Click it to download and install the update — Ollama restarts automatically when it’s done.

To update manually, download the latest installer from ollama.com and run it. The installer handles the update cleanly — it stops the running service, replaces the binary, and starts the updated service. Your models in C:UsersYourName.ollamamodels (or your custom OLLAMA_MODELS path) are not affected.

After updating on Windows, verify in a new terminal:

ollama --version

Do Your Models Need to Be Re-Downloaded After an Update?

No. Models are stored separately from the Ollama binary and are never deleted or modified during an update. The model files in your models directory stay exactly as they are — the update only replaces the Ollama server binary itself.

There’s one nuance: occasionally Ollama updates the model manifest format or adds new quantization support. In these cases, you might see slightly different behaviour when pulling new versions of models, but existing downloaded models continue to work as-is. You don’t need to re-download anything unless you specifically want a newer version of a model.

What Actually Changes Between Versions

Ollama’s release cadence is fast — sometimes multiple releases in a week. Not every update is worth rushing to apply, but some are genuinely significant. Here’s what typically shows up in changelogs and when it matters:

New model support. When a major model is released (a new Llama version, Qwen update, DeepSeek release), Ollama usually updates within days to support it properly — optimised quantization configs, correct context lengths, and model-specific parameters. If you’re trying to pull a brand-new model and it’s behaving oddly, an Ollama update often fixes it.

GPU detection improvements. NVIDIA driver compatibility, AMD ROCm support, and Apple Metal optimizations get updated regularly. If your GPU wasn’t being picked up properly, updating Ollama is one of the first things to try before diving into driver troubleshooting.

Performance improvements. Flash attention support, better KV cache management, and parallelism improvements show up in releases and can meaningfully increase tokens per second without any configuration changes on your part.

Bug fixes. Context length handling, memory leaks, API endpoint behaviour, and streaming reliability are all areas that get patched in minor releases. If something stopped working after a macOS, Windows, or Ubuntu system update, an Ollama update often restores it.

New API features. Tool calling support improvements, structured output handling, and new API endpoints (like the OpenAI-compatible /v1/ routes) have all been added in point releases. If you’re building on the API, checking release notes occasionally is worth it.

Rolling Back to a Previous Version

Occasionally an update introduces a regression — something that worked in the previous version stops working. Rolling back is possible, though it requires a bit more manual work than updating.

On Linux, download a specific version’s binary from the GitHub releases page and replace the current one:

# Find the version you want at github.com/ollama/ollama/releases
# Download the specific Linux amd64 binary
sudo curl -L https://github.com/ollama/ollama/releases/download/v0.x.x/ollama-linux-amd64   -o /usr/local/bin/ollama
sudo chmod +x /usr/local/bin/ollama
sudo systemctl restart ollama

On macOS, download the .dmg for the specific version from GitHub releases, install it over the current version. On Windows, the GitHub releases page has versioned installers — run the older one and it’ll replace the current install.

Worth reporting the regression on the Ollama GitHub issues page too — the maintainers are responsive and regressions usually get patched quickly in a follow-up release.

Keeping Ollama Updated on a Server

If you’re running Ollama on a Linux server you access remotely, automating updates is useful but needs a bit of thought. A simple approach is a weekly cron job:

# Add to crontab with: crontab -e
# Runs every Monday at 3am
0 3 * * 1 curl -fsSL https://ollama.com/install.sh | sh >> /var/log/ollama-update.log 2>&1

This keeps Ollama current without manual intervention. The downside is you have no control over when updates happen or whether they might introduce a breaking change for your running applications. For anything business-critical, a staged approach is safer: update on a dev instance first, confirm nothing broke, then update production.

Check the update log occasionally to make sure it’s running cleanly:

tail -50 /var/log/ollama-update.log

After Updating: Quick Sanity Check

After any Ollama update, it’s worth running a quick check to confirm everything is still working as expected:

# Confirm new version
ollama --version

# Confirm service is running (Linux)
systemctl status ollama

# Confirm models are still listed
ollama list

# Quick inference test
ollama run llama3.2 "Say hello in one sentence"

If the inference test responds normally, you’re good. If something’s broken, check journalctl -u ollama -n 50 on Linux or the Event Viewer on Windows for error details. Most post-update issues are either a service that didn’t restart cleanly (fix: restart the service) or a driver compatibility change (fix: check GitHub issues for your GPU/OS combination).

Ollama moves fast as a project — the team ships improvements frequently and the community is quick to flag regressions. Staying roughly current (within a few releases) means you benefit from model support and performance improvements without much risk. Staying multiple major versions behind is where you start running into issues pulling new models or connecting newer tools.

Staying Informed About New Releases

The best way to know when a meaningful update lands is to watch the Ollama GitHub repository. Go to github.com/ollama/ollama, click Watch → Custom, and enable “Releases” notifications. You’ll get an email whenever a new version is published — useful for staying on top of major updates without checking manually.

The Ollama Discord and Reddit community at r/LocalLLaMA are also good signal sources. When a release has a notable performance improvement or a significant new feature, these communities surface it quickly with real-world benchmark comparisons. That context helps you judge whether an update is worth the two-minute upgrade process or whether you can skip a release without missing much.

Updating Ollama Models (Separate from Ollama Itself)

One thing worth clarifying: updating Ollama (the server) is separate from updating your models. Model files don’t update automatically — when a new version of Llama or Mistral is released in the Ollama library, you pull it explicitly:

# Pull the latest version of a model
ollama pull llama3.2

# Pull a specific version tag
ollama pull llama3.2:3b-instruct-q4_K_M

# See available tags for a model
# Visit ollama.com/library/llama3.2 in your browser

Running ollama pull modelname on an already-downloaded model checks whether a newer version is available and downloads only the changed layers if so — similar to how Docker handles image updates. If you’re already on the latest version, it tells you and downloads nothing. It’s safe to run ollama pull on any model periodically to stay current with model improvements, especially for actively maintained models like Llama and Qwen that receive frequent quantization and configuration updates.

Between updating Ollama itself and periodically pulling fresh model versions, you’re keeping both the runtime and the models current — and that combination means you’re always getting the best performance the project has to offer on your hardware.

Troubleshooting a Failed Update

Updates occasionally fail, usually due to a network interruption or a permissions issue. On Linux, if the install script errors out, re-run it — it’s idempotent and picks up where it left off. Check systemctl status ollama afterwards; if it shows a failed state, run sudo systemctl start ollama to attempt a manual restart and check the logs with journalctl -u ollama -n 30 for error detail. On Windows, if the new installer fails, try right-clicking it and running as Administrator. On Mac, if the app won’t update, download the fresh .dmg directly from ollama.com rather than relying on the in-app update mechanism. In all cases, your models are safe — they live in a separate directory that the update process never touches, so even a failed update doesn’t put your downloaded models at risk.