ollama/docs/troubleshooting.md

How to troubleshoot issues

Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on Mac by running the command:

cat ~/.ollama/logs/server.log

On Linux systems with systemd, the logs can be found with this command:

journalctl -u ollama

When you run Ollama in a container, the logs go to stdout/stderr in the container:

docker logs <container-name>

(Use docker ps to find the container name)

If manually running ollama serve in a terminal, the logs will be on that terminal.

When you run Ollama on Windows, there are a few different locations. You can view them in the explorer window by hitting <cmd>+R and type in:

• explorer %LOCALAPPDATA%\Ollama to view logs
• explorer %LOCALAPPDATA%\Programs\Ollama to browse the binaries (The installer adds this to your user PATH)
• explorer %HOMEPATH%\.ollama to browse where models and configuration is stored
• explorer %TEMP% where temporary executable files are stored in one or more ollama* directories

To enable additional debug logging to help troubleshoot problems, first Quit the running app from the tray menu then in a powershell terminal

$env:OLLAMA_DEBUG="1"
& "ollama app.exe"

Join the Discord for help interpreting the logs.

LLM libraries

Ollama includes multiple LLM libraries compiled for different GPUs and CPU vector features. Ollama tries to pick the best one based on the capabilities of your system. If this autodetection has problems, or you run into other problems (e.g. crashes in your GPU) you can workaround this by forcing a specific LLM library. cpu_avx2 will perform the best, followed by cpu_avx an the slowest but most compatible is cpu. Rosetta emulation under MacOS will work with the cpu library.

In the server log, you will see a message that looks something like this (varies from release to release):

Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]

Experimental LLM Library Override

You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:

OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve

You can see what features your CPU has with the following.

cat /proc/cpuinfo| grep flags  | head -1

AMD Radeon GPU Support

Ollama leverages the AMD ROCm library, which does not support all AMD GPUs. In some cases you can force the system to try to use a similar LLVM target that is close. For example The Radeon RX 5400 is gfx1034 (also known as 10.3.4) however, ROCm does not currently support this target. The closest support is gfx1030. You can use the environment variable HSA_OVERRIDE_GFX_VERSION with x.y.z syntax. So for example, to force the system to run on the RX 5400, you would set HSA_OVERRIDE_GFX_VERSION="10.3.0" as an environment variable for the server. If you have an unsupported AMD GPU you can experiment using the list of supported types below.

At this time, the known supported GPU types are the following LLVM Targets. This table shows some example GPUs that map to these LLVM targets:

LLVM Target	An Example GPU
gfx900	Radeon RX Vega 56
gfx906	Radeon Instinct MI50
gfx908	Radeon Instinct MI100
gfx90a	Radeon Instinct MI210
gfx940	Radeon Instinct MI300
gfx941
gfx942
gfx1030	Radeon PRO V620
gfx1100	Radeon PRO W7900
gfx1101	Radeon PRO W7700
gfx1102	Radeon RX 7600

AMD is working on enhancing ROCm v6 to broaden support for families of GPUs in a future release which should increase support for more GPUs.

Reach out on Discord or file an issue for additional help.

Installing older versions on Linux

If you run into problems on Linux and want to install an older version you can tell the install script which version to install.

curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.27" sh