Those models are for general use. If you have business use case and data related to it you can finetune model for specific use that will outperform all of frontier models and run at fraction of cost.
You can’t right now because there is no dataset available for fine tuning. I’m just saying that it’s possible to fine tune 8B-35B parameter model in int4 that will outperform those models ex. for single programming language and developer specific problems. You can read example of fine tuned 8B model here https://fermisense.com/when-machines-take-the-wheel/
Running model that is good at everything require huge amount of energy and huge data center. Those models are mixture of experts. Latest Kimi K3 have 896 experts. Imagine you have company with 896 employees. Each question involves 16 employees to figure out what to do in what area of your business. Like a brainstorm to solve problem. Now if you know exactly what you want and in which area you actually need only 1-5 people. Like an agile team instead of all those people that you have. So you can hire just couple Kimi K3 experts. 16 experts are 100B parameters so roughly 1 expert in frontier open source model is 6B parameters. 5 experts is 30B parameters. You can run 27B Qwen 3.6 quantized into int4 on your computer like other people are doing right now.
I posted link below to example where they fine tuned model ( take it like a employee training ) for specific task.
An important caveat to that explanation is that the mixture of experts gets re-evaluated for every single token in the input sequence and not for high level tasks as in the example. There will be certain “experts” for looking at indentation tokens, or ones that look at specific word beginnings/prefixes etc.
Those models are for general use. If you have business use case and data related to it you can finetune model for specific use that will outperform all of frontier models and run at fraction of cost.
that’s the other huge advantage of open models you can run locally
How can you run a local model for better coding support than fable/opus?
You can’t right now because there is no dataset available for fine tuning. I’m just saying that it’s possible to fine tune 8B-35B parameter model in int4 that will outperform those models ex. for single programming language and developer specific problems. You can read example of fine tuned 8B model here https://fermisense.com/when-machines-take-the-wheel/
How do you get into this? Any articles you can share? OP says you can run on your desktop… How? Doesn’t this stuff require huge data centers?
Running model that is good at everything require huge amount of energy and huge data center. Those models are mixture of experts. Latest Kimi K3 have 896 experts. Imagine you have company with 896 employees. Each question involves 16 employees to figure out what to do in what area of your business. Like a brainstorm to solve problem. Now if you know exactly what you want and in which area you actually need only 1-5 people. Like an agile team instead of all those people that you have. So you can hire just couple Kimi K3 experts. 16 experts are 100B parameters so roughly 1 expert in frontier open source model is 6B parameters. 5 experts is 30B parameters. You can run 27B Qwen 3.6 quantized into int4 on your computer like other people are doing right now.
I posted link below to example where they fine tuned model ( take it like a employee training ) for specific task.
An important caveat to that explanation is that the mixture of experts gets re-evaluated for every single token in the input sequence and not for high level tasks as in the example. There will be certain “experts” for looking at indentation tokens, or ones that look at specific word beginnings/prefixes etc.