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using github copilot cli with local models lm studio dev community skip to content navigation menu search powered by algolia search log in create account dev community close add reaction like unicorn exploding head raised hands fire jump to comments save boost more copy link copy link copied to clipboard share to x share to linkedin share to facebook share to mastodon share post via report abuse emanuele bartolesi for playful programming posted on apr 9 using github copilot cli with local models lm studio github githubcopilot ai a lap around github copilot 2 part series 1 using github copilot cli with local models lm studio 2 using github copilot cli with azure ai foundry byok models part 2 local ai is getting attention for one simple reason control cloud models are strong and fast but for many companies and developers especially when experimenting or working with sensitive code that is not ideal this is where local models come in tools like lm studio let you run llms directly on your machine no external calls no data leaving your environment or your network instead of sending prompts to cloud models you can point copilot cli to a local model running in lm studio this setup is not perfect it is not officially seamless but it works well enough for learning experimentation and some real workflows what you need before setting this up make sure the basics are clear this is not a plug and play setup there are a few moving parts and some assumptions github copilot cli you need github copilot cli installed and working you can launch github copilot cli with the following command copilot enter fullscreen mode exit fullscreen mode or even better if you want to see the banner in the terminal copilot banner enter fullscreen mode exit fullscreen mode by default the cli uses github managed models but now you can override that note you must be authenticated with github and have access to copilot lm studio lm studio is the simplest way to run local llms without dealing with raw model tooling what it gives you a ui to download and run models a local server that exposes an openai compatible api no need to manually manage inference engines once running it exposes an endpoint like openai compatible http localhost 1234 v1 enter fullscreen mode exit fullscreen mode this is the key piece copilot cli will talk to this endpoint instead of the cloud a local model be realistic not all models are equal and your hardware matters for this guide a small model like qwen qwen3 coder 30b enter fullscreen mode exit fullscreen mode is enough to get started but be clear on the trade offs small models fast but weaker reasoning large models better output but slow or unusable on most laptops if you are on a standard laptop 1b 3b models ok 7b models borderline 13b usually not practical without a gpu on my laptop i am giving a chance to the nvidia model nemotron 3 nano 4b but on my gaming pc that i use for developing and not gaming i use bigger models la qwen3 code or similar tip start small the goal here is understanding the workflow not benchmarking models connecting copilot cli to lm studio this is the part most people get wrong copilot cli is not designed primarily for local models you are using a byok bring your own key model path that is still evolving it works but you need to be precise reference https docs github com en copilot how tos copilot cli customize copilot use byok models 1 set the environment variables you must override the default copilot provider in powershell env copilot_provider_base_url http localhost 1234 v1 env copilot_model google gemma 3 1b env copilot_offline true enter fullscreen mode exit fullscreen mode what they do copilot_provider_base_url points to your local lm studio server copilot_model defines which model to use copilot_offline prevents fallback to cloud models if copilot_offline is not set copilot may silently use cloud models 2 run a simple test open lm studio and open the developer tab click on the switch of the status and the server will start in a second then click on load models and load the models you prefer that you downloaded previously on your machine open copilot cli with the following command in a project folder you want to test copilot banner enter fullscreen mode exit fullscreen mode ask a simple tasks like give me the list of all the files larger than 2mb if everything is configured correctly the response comes from your local model no external api calls are made don t expect a result in seconds like a normal cloud model but it depends on your hardware it can also take minutes sometimes reality check this is not a first class integration no guarantee of full compatibility no optimization for local inference no smart routing like in github hosted copilot but for simple cli workflows it is good enough when this setup makes sense do not use this everywhere use it where it actually gives you an advantage 1 privacy sensitive environments if code cannot leave your machine this setup is useful examples internal tools proprietary scripts regulated environments you avoid sending prompts and contexts to external services this is the strongest reason to use local models especially in some companies like insurances or military it makes absolutely sense 2 offline workflows if you work without a stable connection or you don t have a connection at all like during a flight it is slower but always available 3 learning and understanding ai this setup forces you to see how llms actually behave you learn prompt sensitivity model limitations output variability this is valuable if you want to go beyond just using copilot when it does not make sense avoid this setup if you need high accuracy large context handling production grade reliability in those cases cloud models are still the better option github copilot quota visibility in vs code if you use github copilot and ever wondered what plan you re on whether you have limits how much premium quota is left when it resets i built a small vs code extension called copilot insights it shows copilot plan and quota status directly inside vs code no usage analytics no productivity scoring just clarity vs code marketplace https marketplace visualstudio com items itemname emanuelebartolesi vscode copilot insights a lap around github copilot 2 part series 1 using github copilot cli with local models lm studio 2 using github copilot cli with azure ai foundry byok models part 2 top comments 0 subscribe personal trusted user create template templates let you quickly answer faqs or store snippets for re use submit preview dismiss code of conduct report abuse are you sure you want to hide this comment it will become hidden in your post but will still be visible via the comment s permalink hide child comments as well confirm for further actions you may consider blocking this person and or reporting abuse playful programming follow practice programming with magically majestic methods read our welcome letter which is an open invitation for you to join join us as a writer more from playful programming stop prompting start thinking ai devrel productivity tooling using github copilot cli with azure ai foundry byok models part 2 ai github githubcopilot programming github copilot is too nice fix it with a tone of voice file github githubcopilot ai programming dev diamond sponsors thank you to our diamond sponsors for supporting the dev community google ai is the official ai model and platform partner of dev neon is the official database partner of dev algolia is the official search partner of dev dev community a space to discuss and keep up software development and manage your software career home dev videos dev education tracks dev challenges dev help advertise on dev organization accounts dev showcase about contact free postgres database dev shop mlh code of conduct privacy policy terms of use built on forem the open source software that powers dev and other inclusive communities made with love and ruby on rails dev 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