The kind of specs that would enable local LLM usage were available on laptops since 2020. The floor for acceptable performance for LLMs stopped literally just before 24+gb of VRAM would have become pretty common in consumer applications. The reason this technology isn’t more ubiquitously available is because ‘they’ thought it would be more profitable to hoover up literally every byte of RAM and GPU in the world rather than let the technology continue to naturally evolve to a place where any consumer could have run useful models on their home computers. In other words, your anger is misplaced.
Probably Qwen Flash. Supposedly the 27b is nearly to capacity when it comes to training potential, while the Flash has still quite undercooked. Despite this, Flash is pretty dang good at understanding my goals and implementing my will, most of the time.
Open models will still be available via API from third-party hosts. The bigger ones like Kimi K3 or GLM 5.3 don’t fit on conventional desktop computers to begin with, most non-institutional users of those would already be doing that (eg via OpenRouter).
There’s not much skill to be had there - It is literally just downloading an app nowadays if you do not want to do anything special, especially on Linux (Newelle is great stuff!). But I am sure there are similar packages available for Windows too.
Excuse my ignorance - I don’t have much experience with AI to this point - but what does Newelle have that something like Unsloth or LMStudio does not? What makes it great stuff?
honestly? ease of use. I like Unsloth too, but Newelle is really an easy solution if you just want to use LLMs on you own hardware (or use inference providers) that requires practically no knowledge at all - it is focused on providing something to use LLMs without anything else interfering.
Unsloth does well too, but if i hadn’t learnt as much as i already did when i started using it, i would probably have been overwhelmed (outside of it crashing pathetically when following symlinks for local model selection - you can define these directories, but the quick selection then just makes it crash)
Np. I actually use Newelle with an fork of llama.cpp that introduces the ability to use ternary models - these work a lot better under tight memory constraints, and achieve performance similar to models with 2-3 times the parameters. Was as easy as installing the fork, starting it and pointing Newelle there instead of the integrated llama.cpp. You can do that with Unsloth too of course, but it’s not that smooth.
Anyone who’s privileged enough to have the hardware.
The kind of specs that would enable local LLM usage were available on laptops since 2020. The floor for acceptable performance for LLMs stopped literally just before 24+gb of VRAM would have become pretty common in consumer applications. The reason this technology isn’t more ubiquitously available is because ‘they’ thought it would be more profitable to hoover up literally every byte of RAM and GPU in the world rather than let the technology continue to naturally evolve to a place where any consumer could have run useful models on their home computers. In other words, your anger is misplaced.
Yeah the cross over point right now sis somewhere between qwen3.8 27b and glm 5.2/5.3
Like qwen is so close to being good.
Probably Qwen Flash. Supposedly the 27b is nearly to capacity when it comes to training potential, while the Flash has still quite undercooked. Despite this, Flash is pretty dang good at understanding my goals and implementing my will, most of the time.
What’s your assumption about where my anger is directed?
Open models will still be available via API from third-party hosts. The bigger ones like Kimi K3 or GLM 5.3 don’t fit on conventional desktop computers to begin with, most non-institutional users of those would already be doing that (eg via OpenRouter).
Anyone who lacks the skill to to it themselves…
There’s not much skill to be had there - It is literally just downloading an app nowadays if you do not want to do anything special, especially on Linux (Newelle is great stuff!). But I am sure there are similar packages available for Windows too.
Excuse my ignorance - I don’t have much experience with AI to this point - but what does Newelle have that something like Unsloth or LMStudio does not? What makes it great stuff?
honestly? ease of use. I like Unsloth too, but Newelle is really an easy solution if you just want to use LLMs on you own hardware (or use inference providers) that requires practically no knowledge at all - it is focused on providing something to use LLMs without anything else interfering.
Unsloth does well too, but if i hadn’t learnt as much as i already did when i started using it, i would probably have been overwhelmed (outside of it crashing pathetically when following symlinks for local model selection - you can define these directories, but the quick selection then just makes it crash)
Thanks, I appreciate you taking the time to answer.
Np. I actually use Newelle with an fork of llama.cpp that introduces the ability to use ternary models - these work a lot better under tight memory constraints, and achieve performance similar to models with 2-3 times the parameters. Was as easy as installing the fork, starting it and pointing Newelle there instead of the integrated llama.cpp. You can do that with Unsloth too of course, but it’s not that smooth.
(if you are interested: https://huggingface.co/prism-ml/collections for the ternary models, https://github.com/PrismML-Eng/llama.cpp for the build that allows for ternary models.)
Thanks, I appreciate it. Had not heard of these before.