Hi all!

i have written a couple of posts in the past, i am an illiterate having fun with LLMs and AI in general, who is being pulled in in a deeper hole by the days…

I have extensive experience with Linux (Gentoo lover since 20 years here) i am a sw dev now “promoted” to management, and avid tech user, so not really illiterate, but i know very little about all this LLM game.

I started with OpenWebUI + Ollama and played as an idiot with random models. Then come across an NVIDIA RTX A4000 (16gb VDDR6) and plugged into my I7-8700 server with 64gb RAM. The server has a Intel Corporation CoffeeLake-S GT2 [UHD Graphics 630] too, unused at this time (server is 100% headless anyway).

I am currently installing LocalAI to run llama.cpp and improve my models capability and speed, planning to ditch OpenWebUI and Ollama, if LocalAI + llama.cpp works fine.

My first usage was chatting with random local models. Then i discovered Fooocus and quickly upgraded to ComfyUI. Last, i have set up my SubWave radio station and i am having so much fun…

I have a few questions:

  1. Can i leverage both my NVIDIA and the iGPU at the same time?
  2. If i use the iGPU do i need to fixedly allocate RAM from the BIOS to it? Or will it use system RAM as needed?
  3. Using llama.cpp i want to leverage also CPU usage, since i have 64gb ram (also shared by many more self hosted stuff, tough) is there anything special i need to do to achieve that?
  4. What are a set of models that you guys recommend for my setup? I am currently using qwen2.5-coder:14b-instruct-q5_K_M with ollama, and i am pretty satisfied with it’s coding capabilities, but i want something more general purpose for my SubWave (AI assisted web radio channel)
  5. I might have the opportunity to install a second RTX A4000, identical to the first, on my server (need to check pci-e slot availability and power supply specs), would that make any sense at all?
  6. Power consumption wise, do the NVIDIA cards suck power also when not in active use?
  • brucethemoose@lemmy.worldEnglish
    5·
    23 days ago

    The best you could run on that system, I believe, is Step 3.7 via ik-llama.cpp.

    You don’t want, nor need, the iGPU. Unfortunately nothing is really optimized to use it for hybrid inference, and it’s not very powerful anyway. You’re better off with a CUDA/CPU backend.

    How it would work in practice is:

    • The “dense” parts of a MoE model like Step stay on the GPU.

    • The big, sparse parts stay in CPU RAM.

    • For prompt processing, everything is done on the GPU since the penalty for going over the PCIe bus isn’t too bad.

    • For text generation (generating the actual output), your CPU cores run the sparse part. This is fine. It’s not nearly as slow as you’d think, as this part is really compute lite and AVX accelerated.

    The tricky part is finding appropriate models and quantizations for your setup. You’re looking at something in the 100-170B range, basically, with the experts quantized to 3-4 bits and the dense weights at 6-bit or higher.

    I can look around later, if you’re interested, but I haven’t kept up with model releases in this range other than the Stepfun series.

    On a separate note, I wouldn’t containerize ik_llama.cpp.

    It just wastes precious RAM and compute. It has basically no dependencies, so it’s not really a security risk, and there isn’t much good that bundles models with it.

    Now, the UIs. Containerize those. Those have tons of dependencies, so it’s easier and safer.