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[GH-ISSUE #5459] Error: llama runner process has terminated: signal: aborted on Raspberry PI 3 #3415

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opened 2026-04-12 14:03:23 -05:00 by GiteaMirror · 6 comments
GiteaMirror commented 2026-04-12 14:03:23 -05:00
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Originally created by @iBukkoG104 on GitHub (Jul 3, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/5459

What is the issue?

I installed ollama on my Raspberry PI 3 for some testing, but when I tried to run it with ollama run mistral, it downloaded it as normal but after that it gave me this error:

Error: llama runner process has terminated: signal: aborted

OS

Linux

GPU

Other

CPU

Other

Ollama version

0.1.48

Originally created by @iBukkoG104 on GitHub (Jul 3, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/5459 ### What is the issue? I installed ollama on my Raspberry PI 3 for some testing, but when I tried to run it with `ollama run mistral`, it downloaded it as normal but after that it gave me this error: >Error: llama runner process has terminated: signal: aborted ### OS Linux ### GPU Other ### CPU Other ### Ollama version 0.1.48
GiteaMirror added the bug label 2026-04-12 14:03:23 -05:00
GiteaMirror commented 2026-04-12 14:03:26 -05:00
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@igorschlum commented on GitHub (Jul 5, 2024):

Hi @iBukkoG104, how much RAM does your Raspberry Pi 3 have?

Mistral takes up approximately 4.1GB of memory. To run Mistral on your
Raspberry Pi, you should ideally have at least 8GB of RAM available.

For small devices like the Raspberry Pi, there are tiny models such as
TinyDolphin that might be suitable alternatives.

BTW Ollama should tell you that you don't have enought memory.

<!-- gh-comment-id:2211191793 --> @igorschlum commented on GitHub (Jul 5, 2024): Hi @iBukkoG104, how much RAM does your Raspberry Pi 3 have? Mistral takes up approximately 4.1GB of memory. To run Mistral on your Raspberry Pi, you should ideally have at least 8GB of RAM available. For small devices like the Raspberry Pi, there are tiny models such as TinyDolphin that might be suitable alternatives. BTW Ollama should tell you that you don't have enought memory.
GiteaMirror commented 2026-04-12 14:03:27 -05:00
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@Moonlight1220 commented on GitHub (Jul 7, 2024):

@igorschlum The maximum ram amount for the RPI3 is 1gb therefore incompatible with Ollama

<!-- gh-comment-id:2212456935 --> @Moonlight1220 commented on GitHub (Jul 7, 2024): @igorschlum The maximum ram amount for the RPI3 is 1gb therefore incompatible with Ollama
GiteaMirror commented 2026-04-12 14:03:28 -05:00
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@igorschlum commented on GitHub (Jul 8, 2024):

@Moonlight1220 ollama run tinyllama:1.1b-chat-v0.6-q3_K_S needing only 500Kb of RAM. So yes, it's possible to use Ollama on RPI3. There are multiples videos on youtube explaining that. It could be usefull for many usages.

<!-- gh-comment-id:2214852782 --> @igorschlum commented on GitHub (Jul 8, 2024): @Moonlight1220 ollama run tinyllama:1.1b-chat-v0.6-q3_K_S needing only 500Kb of RAM. So yes, it's possible to use Ollama on RPI3. There are multiples videos on youtube explaining that. It could be usefull for many usages.
GiteaMirror commented 2026-04-12 14:03:28 -05:00
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@Moonlight1220 commented on GitHub (Jul 8, 2024):

@igorschlum I was un-aware that that tinyllama:1.1b-chat-v0.6-q3_K_S was available, thank you for pointing that out and I will make sure to remember that for future issue's.

<!-- gh-comment-id:2215110944 --> @Moonlight1220 commented on GitHub (Jul 8, 2024): @igorschlum I was un-aware that that tinyllama:1.1b-chat-v0.6-q3_K_S was available, thank you for pointing that out and I will make sure to remember that for future issue's.
GiteaMirror commented 2026-04-12 14:03:29 -05:00
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@dhiltgen commented on GitHub (Jul 24, 2024):

We've added logic in recent versions to try to catch the scenario where the model wont fit in total VRAM+system memory so it gives a better error message instead of just crashing, but ultimately you'll need to use one of the tiny models to have a working setup on a RPI.

<!-- gh-comment-id:2246629097 --> @dhiltgen commented on GitHub (Jul 24, 2024): We've added logic in recent versions to try to catch the scenario where the model wont fit in total VRAM+system memory so it gives a better error message instead of just crashing, but ultimately you'll need to use one of the tiny models to have a working setup on a RPI.
GiteaMirror commented 2026-04-12 14:03:30 -05:00
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@epeters3 commented on GitHub (Dec 14, 2024):

I've seen this same error when trying to run a new model (llama3.2) on an outdated version of ollama on my Raspberry PI 5. Upgrading ollama solved the problem.

<!-- gh-comment-id:2542736610 --> @epeters3 commented on GitHub (Dec 14, 2024): I've seen this same error when trying to run a new model (`llama3.2`) on an outdated version of `ollama` on my Raspberry PI 5. Upgrading `ollama` solved the problem.
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