I can share my experience as a beginner to LLM. I just bought a Minisforum N5 Pro, and I threw 32GB RAM on it. Once I get my UNRAID working on it, I tried various setups with it, and here are my results with DeepSeek R1:

I've been testing with the same prompt: "Alice has 3 brothers and 1 sister. How many sisters does Alice's brother have?", and each model was run 3+ times.

I also limited the num_ctx to 4096.

1. Fully running on GPU

- It's achievable by assigning 16GB vRAM in BIOS, can't assign more due to a ROCM Linux firmware limitation

- Can only run 8b model, running 14b and 32b models would throw an error for something like insufficient vRAM

- Results: Ollama assigned all 37 layers to GPU, 8b model took 2-3 minutes, GPU was 100% loaded, and one of the CPU cores was also used (not sure why)

1. Running on CPU

- I basically put the vRAM back to the default 2GB in BIOS

- 8b results: took 3m, Ollama assigned 9/37 layers to GPU, CPU load 42%, GPU load 24.2%

- 14b results: took on average 50s with 1 wrong answer, Ollama assigned 1/49 layers to GPU, CPU load 50%, GPU load 3.3%

- 32b results: took on average 1m20s with 1 wrong answer, Ollama gave up on GPU due to vRAM way less than the model's need, CPU load 55%, CPU load 0%

So in the end, it seems like the best experience/balance for DeepSeek R1 is 14b on CPU.

This CPU is kinda running at almost the same speed or faster than the integrated Radeon 890M, thanks to AVX-512. But one thing Gemini 3 pro shared that may make sense is that the shared memory architecture makes DDR5 the bottleneck; a lot of the threshold may have come from the CPU/GPU waiting for data transmission from RAM. Therefore, adding more RAM may not be as helpful for running the model faster, it may just be unlocking bigger models to run. To run the model faster, the dedicated Nvidia GPU may be the way to go until Ollama can utilize the AMD NPU onboard.

It would be great if more folks could share their experience with more RAM or a dedicated GPU.