I watched one video and read 2 pages of text. So take this with a mountain of salt. From that I gathered that deepseek R1 is the model you interact with when you use the app. The complexity of a model is expressed as the number of parameters (though I don’t know yet what those are) which dictate its hardware requirements. R1 contains 670 bn Parameter and requires very very beefy server hardware. A video said it would be 10th of GPUs. And it seems you want much of VRAM on you GPU(s) because that’s what AI crave. I’ve also read 1BN parameters require about 2GB of VRAM.
Got a 6 core intel, 1060 6 GB VRAM,16 GB RAM and Endeavour OS as a home server.
I just installed Ollama in about 1/2 an hour, using docker on above machine with no previous experience on neural nets or LLMs apart from chatting with ChatGPT. The installation contains the Open WebUI which seems better than the default you got at ChatGPT. I downloaded the qwen2.5:3bn model (see https://ollama.com/search) which contains 3 bn parameters. I was blown away by the result. It speaks multiple languages (including displaying e.g. hiragana), knows how much fingers a human has, can calculate, can write valid rust-code and explain it and it is much faster than what i get from free ChatGPT.
The WebUI offers a nice feedback form for every answer where you can give hints to the AI via text, 10 score rating thumbs up/down. I don’t know how it incooperates that feedback, though. The WebUI seems to support speech-to-text and vice versa. I’m eager to see if this docker setup even offers APIs.
I’ll probably won’t use the proprietary stuff anytime soon.
Vivaldi is my daily driver. It has the best tab-management, dark website-mode (hidden function), build-in tracker, pop-up & ad-blocker, RSS-Reader, e-mail client, site-hibernation and much more. My hope is that the build-in protection will suffice when ublock origin will stop functioning. I can’t use any other browser anymore.
I watched one video and read 2 pages of text. So take this with a mountain of salt. From that I gathered that deepseek R1 is the model you interact with when you use the app. The complexity of a model is expressed as the number of parameters (though I don’t know yet what those are) which dictate its hardware requirements. R1 contains 670 bn Parameter and requires very very beefy server hardware. A video said it would be 10th of GPUs. And it seems you want much of VRAM on you GPU(s) because that’s what AI crave. I’ve also read 1BN parameters require about 2GB of VRAM.
Got a 6 core intel, 1060 6 GB VRAM,16 GB RAM and Endeavour OS as a home server.
I just installed Ollama in about 1/2 an hour, using docker on above machine with no previous experience on neural nets or LLMs apart from chatting with ChatGPT. The installation contains the Open WebUI which seems better than the default you got at ChatGPT. I downloaded the qwen2.5:3bn model (see https://ollama.com/search) which contains 3 bn parameters. I was blown away by the result. It speaks multiple languages (including displaying e.g. hiragana), knows how much fingers a human has, can calculate, can write valid rust-code and explain it and it is much faster than what i get from free ChatGPT.
The WebUI offers a nice feedback form for every answer where you can give hints to the AI via text, 10 score rating thumbs up/down. I don’t know how it incooperates that feedback, though. The WebUI seems to support speech-to-text and vice versa. I’m eager to see if this docker setup even offers APIs.
I’ll probably won’t use the proprietary stuff anytime soon.