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Comment by nijave | Hacker News Reader

Anyone compare to ollama? I had good success with latest ollama with ROCm 7.4 on 9070 XT a few days ago

It is optimized for compatibility across different APIs as well as has specific hardware builds for AMD GPUs and NPUs. It’s run by AMD.

Under the hood they are both running llama.cpp, but this has specific builds for different GPUs. Not sure if the 9070 is one, I am running it on a 370 and 395 APU.

I just compared this on my Mac book M1 Max 64GB RAM with the following:

Model: qwen3.59b Prompt: "Hey, tell me a story about going to space"

Ollama completed in about 1:44 Lemonade completed in about 1:14

So it seems faster in this very limited test.

I'm also curious about this one, also I want to compare this to vLLM.

Seconded. Currently on ollama for local inference, but I am curious how it compares.

Lemonade is using llama.cpp for text and vision with a nightly ROCm build. It can also load and serve multiple LLMs at the same time. It can also create images, or use whisper.cpp, or use TTS models, or use NPU (e.g Strix Halo amdxdna2), and more!

better than Vulkan?

In my experience using llama.cpp (which ollama uses internally) on a Strix Halo, whether ROCm or Vulkan performs better really depends on the model and it's usually within 10%. I have access to an RX 7900 XT I should compare to though.

Perhaps I should just google it, but I'm under the impression that ollama uses llama.cpp internally, not the other way around.

Thanks for that data point I should experiment with ROCm

I meant ollama uses llama.cpp internally. Sorry for the confusion.

From what I understand, ROCm is a lot buggier and has some performance regressions on a lot of GPUs in the 7.x series. Vulkan performance for LLMs is apparently not far behind ROCm and is far more stable and predictable at this time.

For me Vulkan performs better on integrated cards, but ROCm (MIGraphX) on 7900 XTX.

As I understand it, it depends on your GPU and ROCm version but they're similar-ish

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