Running Local LLM isn't really a lot effort anymore, since its one command away to get the familar interface of chat AI. This setup requires 24GB ish VRAM and 32GB ram, so if you have capable gaming PC which can do AAA games, you can run it.
After that simply open browser and enter: http://localhost:8080
What this does: This will download Gemma4 AI with 26B param & start a http server for chat
Its shockingly capable for its size. Does it beat the top end models? No, but as long your don't fall into the hallucations. Its just fine.
Edit: the software is llama.cpp you can download it from "releases" which u can find at github right side. No need to know how to build it
Edit2: Pro tip is, only use chat per context you want to use. Lots users want "dynamically" change the context, but that doesnt really work from my experience.
Not sure what kind of model you're interested in, but you may give Duck.ai[0] a try. It doesn't require any signup and let you use gpt-5.4 nano/mini, Haiku 4.5, Mistral Small 4 and Gemma 4 31B.
The hardest part isn’t payment; the prompt itself may identify you. For truly sensitive topics, I’d abstract the details first, then use a local model—or avoid an LLM entirely.
Can you elaborate on the first step? Which cloud and which service? What's the cost outlay if you are just having a convo and not doing anything 'agentic'?
It depends, but usually spin up an h100 on lambda.ai or coreweave. They have capacity and their UIs/APIs are nice. I spin it up for an hour or two, believe it was 6~ dollars an hour.
Running Local LLM isn't really a lot effort anymore, since its one command away to get the familar interface of chat AI. This setup requires 24GB ish VRAM and 32GB ram, so if you have capable gaming PC which can do AAA games, you can run it.
- llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M
After that simply open browser and enter: http://localhost:8080
What this does: This will download Gemma4 AI with 26B param & start a http server for chat
Its shockingly capable for its size. Does it beat the top end models? No, but as long your don't fall into the hallucations. Its just fine.
Edit: the software is llama.cpp you can download it from "releases" which u can find at github right side. No need to know how to build it
Edit2: Pro tip is, only use chat per context you want to use. Lots users want "dynamically" change the context, but that doesnt really work from my experience.
hi, you should try https://ollama.com/ which is imo the most convenient way to run local LLMs
Not sure what kind of model you're interested in, but you may give Duck.ai[0] a try. It doesn't require any signup and let you use gpt-5.4 nano/mini, Haiku 4.5, Mistral Small 4 and Gemma 4 31B.
[0] https://duck.ai/
With llama.cpp, Intel i9-13900KS CPU, 96 GB RAM, RTX 4070 running locally.
The models I'm using right now with that are:
The hardest part isn’t payment; the prompt itself may identify you. For truly sensitive topics, I’d abstract the details first, then use a local model—or avoid an LLM entirely.
Maybe try Ollama Cloud, Prompt or response data is never logged or trained on:
https://ollama.com/pricing
I spin up a gpu instance in a cloud, run my model via vllm, connect to it via an ssh tunnel. done.
Can you elaborate on the first step? Which cloud and which service? What's the cost outlay if you are just having a convo and not doing anything 'agentic'?
Hey sure.
It depends, but usually spin up an h100 on lambda.ai or coreweave. They have capacity and their UIs/APIs are nice. I spin it up for an hour or two, believe it was 6~ dollars an hour.
Once the gpu instance is up, you need to run vllm and a model, ie https://docs.lambda.ai/education/large-language-models/deplo....
Then you can connect your pi.dev, openwebui, etc etc to vllm and interact with it like normal.
Best is to use local LLMs obviously. I haven't used it but nano-gpt accepts crypto payments.
https://cake.nano-gpt.com
With a local one
i've been using OpenAI's os PII-masking model, works decently and lightweight enough to run virtually anywhere https://huggingface.co/openai/privacy-filter
I don't
Can't you do it logged out?