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Comment by clusterhacks

12 hours ago

I ran ollama first because it was easy, but now download source and build llama.cpp on the machine. I don't bother saving a file system between runs on the rented machine, I build llama.cpp every time I start up.

I am usually just running gpt-oss-120b or one of the qwen models. Sometimes gemma? These are mostly "medium" sized in terms of memory requirements - I'm usually trying unquantized models that will easily run on an single 80-ish gb gpu because those are cheap.

I tend to spend $10-$20 a week. But I am almost always prototyping or testing an idea for a specific project that doesn't require me to run 8 hrs/day. I don't use the paid APIs for several reasons but cost-effectiveness is not one of those reasons.

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clusterhacks

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Juminuvi  8 hours ago

I know you say you don't use the paid apis, but renting a gpu is something I've been thinking about and I'd be really interested in knowing how this compares with paying by the token. I think gpt-oss-120b is 0.10/input 0.60/output per million tokens in azure. In my head this could go a long way but I haven't used gpt oss agentically long enough to really understand usage. Just wondering if you know/be willing to share your typical usage/token spend on that dedicated hardware?

bigiain  9 hours ago

I don't suppose you have (or would be interested in writing) a blog post about how you set that up? Or maybe a list of links/resources/prompts you used to learn how to get there?

  • clusterhacks  8 hours ago

    No, I don't blog. But I just followed the docs for starting an instance on lambda.ai and the llama.cpp build instructions. Both are pretty good resources. I had already setup an SSH key with lambda and the lambda OS images are linux pre-loaded with CUDA libraries on startup.

    Here are my lazy notes + a snippet of the history file from the remote instance for a recent setup where I used the web chat interface built into llama.cpp.

    I created an instance gpu_1x_gh200 (96 GB on ARM) at lambda.ai.

    connected from terminal on my box at home and setup the ssh tunnel.

    ssh -L 22434:127.0.0.1:11434 ubuntu@<ip address of rented machine - can see it on lambda.ai console or dashboard>

      Started building llama.cpp from source, history:    
     21  git clone   https://github.com/ggml-org/llama.cpp
     22  cd llama.cpp
     23  which cmake
     24  sudo apt list | grep libcurl
     25  sudo apt-get install libcurl4-openssl-dev
     26  cmake -B build -DGGML_CUDA=ON
     27  cmake --build build --config Release

    MISTAKE on 27, SINGLE-THREADED and slow to build see -j 16 below for faster build

         28  cmake --build build --config Release -j 16
     29  ls
     30  ls build
     31  find . -name "llama.server"
     32  find . -name "llama"
     33  ls build/bin/
     34  cd build/bin/
     35  ls
     36  ./llama-server -hf ggml-org/gpt-oss-120b-GGUF -c 0 --jinja

    MISTAKE, didn't specify the port number for the llama-server

         37  clear;history
     38  ./llama-server -hf Qwen/Qwen3-VL-30B-A3B-Thinking -c 0 --jinja --port 11434
     39  ./llama-server -hf Qwen/Qwen3-VL-30B-A3B-Thinking.gguf -c 0 --jinja --port 11434
     40  ./llama-server -hf Qwen/Qwen3-VL-30B-A3B-Thinking-GGUF -c 0 --jinja --port 11434
     41  clear;history

    I switched to qwen3 vl because I need a multimodal model for that day's experiment. Lines 38 and 39 show me not using the right name for the model. I like how llama.cpp can download and run models directly off of huggingface.

    Then pointed my browser at http//:localhost:22434 on my local box and had the normal browser window where I could upload files and use the chat interface with the model. That also gives you an openai api-compatible endpoint. It was all I needed for what I was doing that day. I spent a grand total of $4 that day doing the setup and running some NLP-oriented prompts for a few hours.