You would get similar performance. Lemonade is designed as a turnkey (optimized for AMD Hardware) for local AI models. The software helps you manage backends (llama.cpp, flm, whispercpp, stable‑diffusion.cpp, etc) for different GenAI modalities from a single utility.

On the performance side, lemonade comes bundled with ROCm and Vulkan. These are sourced from https://github.com/lemonade-sdk/llamacpp-rocm and https://github.com/ggml-org/llama.cpp/releases respectively.

I have two Strix Halo devices at hand. Privately a framework desktop with 128gb and at work 64GB HP notebook. The 64GB machine can load Qwen3.5 30B-A3B, with VSCode it needs a bit of initial prompt processing to initialize all those tools I guess. But the model is fighting with the other resources that I need. So I am not really using it anymore these days, but I want to experiment on my home machine with it. I just dont work on it much right now.

Lemonade has a Web UI to set the context size and llama.cpp args, you need to set context to proper number or just to 0 so that it uses the default. If its too low, it wont work with agentic coding.

I will try some Claw app, but first need to research the field a bit. But I am using different models on Open Web UI. GPT 120B is fast, but also Qwen3.5 27B is fine.

As another data point.

Running Qwen3.5 122B at 35t/s as a daily driver using Vulcan llama.cpp on kernel 7.0.0rc5 on a Framework Desktop board (Strix Halo 128).

Also a pair of AMD AI Pro r9700 cards as my workhorses for zimageturbo, qwen tts/asr and other accessory functions and experiments.

Finally have a Radeon 6900 XT running qwen3.5 32B at 60+t/s for a fast all arounder.

If I buy anything nvidia it will be only for compatibility testing. AMD hardware is 100% the best option now for cost, freedom, and security for home users.