Author Topic: Running Ollama on PPC64LE  (Read 376 times)

icbts

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Running Ollama on PPC64LE
« on: February 18, 2025, 11:11:59 am »
After reading the great post on VivaPowerPC about running Ollama on PPC64LE, I got excited and made a video following their steps, and the results I got on my Raptor Blackbird (yes, there is a shout out to VivaPowerPC for their excellent article). Wanted to share a link with the community :)

Has anyone else run Ollama on their PPC64LE rigs? Have benchmark results to share?
If I can get another RTX A1000 (PCIe x8) card I'll see if i can get the Nvidia ppc64le drivers to work with Ollama.

How to run Ollama on PPC64LE:
https://www.youtube.com/watch?v=P4iEZiwfLm8

Borley

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Re: Running Ollama on PPC64LE
« Reply #1 on: February 18, 2025, 10:36:22 pm »
The article in question. If it can be built and run in an entirely directory contained way, I will give it a try. I'm still running a Polaris generation AMD GPU, so it may or may not be worth trying with acceleration.

witsu

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Re: Running Ollama on PPC64LE
« Reply #2 on: February 19, 2025, 06:56:46 pm »
Yea I was able to get Ollama up and running following the instructions.
I didn't get very good performance though. I was getting only around  0.10 tokens/s using deepseek-r1:32b on CPU only.

I did try to get my RTX A4000 GPU to work but I couldn't get the driver to work properly. nvidia-smi just hangs and eventually gives an error message.

adaptl

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Re: Running Ollama on PPC64LE
« Reply #3 on: February 19, 2025, 10:55:30 pm »
I got llama.cpp working with vulkan and on CPU alone, and I also noticed poor performance

atomicdog

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Re: Running Ollama on PPC64LE
« Reply #4 on: February 20, 2025, 12:18:45 am »
For the llama3.2, CPU only, on my blackbird (4-core, 32GB) I get 5.94 tokens/s but on my TalosII (18-core, 368GB) I only get 0.34 tokens/s
« Last Edit: February 20, 2025, 12:21:23 am by atomicdog »

rheaplex

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Re: Running Ollama on PPC64LE
« Reply #5 on: February 21, 2025, 10:37:50 pm »
llama.cpp with Vulkan is good on my older AMD card as long as the model fits in its VRAM and I supply the correct command line arguments (the llama.cpp wiki has good examples).