What is currently the best LLM model for consumer grade hardware? Is it phi-4?

1 month ago 2

Actually DeepSeek-R1-0528-Qwen3-8B was uploaded yesterday (Thursday) at 11 AM UTC / 7 PM CST. AI moves fast! Your "today" comment made me have to check if a new version came out! ;D

> Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.

Thank you for thinking of the vibe coders.

I'm afraid that 1) you are not going to get a definite answer, 2) an objective answer is very hard to give, 3) you really need to try a few most recent models on your own and give them the tasks that seem most useful/meaningful to you. There is drastic difference in output quality depending on the task type.

I have an RTX 3070 with 8GB VRAM and for me Qwen3:30B-A3B is fast enough. It's not lightning fast, but more than adequate if you have a _little_ patience.

I've found that Qwen3 is generally really good at following instructions and you can also very easily turn on or off the reasoning by adding "/no_think" in the prompt to turn it off.

The reason Qwen3:30B works so well is because it's a MoE. I have tested the 14B model and it's noticeably slower because it's a dense model.

I think you'll find that on that card most models that are approaching the 16G memory size will be more than fast enough and sufficient for chat. You're in the happy position of needing steeper requirements rather than faster hardware! :D

Ollama is the easiest way to get started trying things out IMO: https://ollama.com/

Good question. I've had some success with Qwen2.5-Coder 14B, I did use the quantised version: huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct-GGUF:latest It worked well on my MacBook Pro M1 32Gb. It does get a bit hot on a laptop though.

I only have 8gb of vram to work with currently, but I'm running OpenWebUI as a frontend to ollamma and I have a very easy time loading up multiple models and letting them duke it out either at the same time or in a round robin.

You can even keep track of the quality of the answers over time to help guide your choice.

https://openwebui.com/

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