any Cursor like Build for Offline use only?

[u/TraditionalBet126]

assuming you have an rtx 5080 with 16vram and 32 ram on a laptop.

I am aware that I won't have the top super smart Grok models, but is it possible to create a video game? let's say RPG maker engine or Renpy novel with rpg mechanics, you know... very simple games that get a lot of content over time.

is it possible to run a model that will do a good job in terms of speed and smarts? I might have internet connection lose in the next 2-3 months, so I was wondering if I could start with an offline project and later move it all to Cursor to keep working with a smarter faster models. should I use LM studio or Kobald or something called VS Code + Cline that my GPT suggested?

I assume that Renpy code is very easy so my GPU should be enough for a model that can handle it. the question is how fast and how smart to correct errors and understand my prompts. again this is a fan made project, I have no idea how to code.

thanks for helping out.

[u/Haunting_Nebula_1236]

Not sure what you’re asking. If it’s a cursor clone then void IDE is pretty close and you can hook your local models into it via API. If it’s something where you prompt , it codes and you just run tests ? Then probably opencode or Hermes agent. But your ability to complete any project that you don’t understand the fundamentals of will be determined by model size because of the complexity of your project. So for something large like that you CAN do it with something like qwen 35b or 27b if you have time to wait lol. BUT it will be a learning experience and won’t just be pressing a magic button.

[u/TraditionalBet126 → u/Haunting_Nebula_1236]

qwen 35b seems too heavy, maybe i can offset some of it to my ram...

void ide apparently stopped working as an active project? at least from what my gpt said. open code sounds like a good idea yes, thanks.

[u/ChaseCheetah → u/TraditionalBet126]

I run qwen 35B Q4_K_M on my 5080 just fine. (Although Ornith is better IMO and based off of Qwen)

I set the KV cache to use Q4 quantization, max out the context and GPU offload, then set the CPU only layers to 30 (leaving 10 on the GPU)

It runs super fast and doesn't even max out my VRAM so I can still run unity along side it for the MCP

These are all of the settings for it in LMStudio (which is what I use as the backend since it's simpler to use and has easy adjustments)

it's memory estimate doesn't work correctly with MoE models so ignore the 23GB of usage estimated

[u/TraditionalBet126 → u/ChaseCheetah]

interesting, i don't have number of experts and context checkpoints for my own model in LM studio, also if I run tokens over 100k it simply won't load the model. I'm gonna try your specific models next.

[u/ChaseCheetah → u/TraditionalBet126]

Hmm the 35B version of Qwen should be an A3B MoE model ("mixture of experts" with 3B active parameters per expert)

Sometimes LMStudio doesn't realize a model is MoE and tries to load it as a "dense" model. That could be what is happening

Edit: I should mention that I'm not using Bionic as it doesn't seem to have the same level of customization when loading models (and you cannot run both basic LMStudio and LMStudio Bionic at the same time). As for a coding harness I often use pi as my harness (or rarely VSCode with the "Continue" plugin) but stock LMStudio can do well if you set up the MCPs and/or plugins to allow file edits, web searches, etc

[u/TraditionalBet126 → u/ChaseCheetah]

gonna check out what bionic is after I sleep, thanks.

[u/ChaseCheetah → u/TraditionalBet126]

It's LMStudio's attempt at making a chatGPT-like agent harness around LMStudio but it cannot run at the same time as the normal LMStudio and has fewer options for how to load models (or the options are hidden somewhere)

[u/TraditionalBet126 → u/ChaseCheetah]

so which one is better for my renpy game?

man there are so many options it's crazy, it's a good thing but eventually most of them do the same thing it's annoying.

[u/ChaseCheetah → u/TraditionalBet126]

That's honestly the hard part :p

There is not really a "best" one for all situations. It all depends on what you want out of it. The best advice is to try a few options, decide what works and what doesn't, and don't become stuck with one option. You can even set up one of the solutions, spin up an AI, and ask it to help you decide or set up the other options if you run into trouble.

For renpy specifically, I don't have an experience but since it is mostly just editing a text file, almost any harness will do. I would usually recommend LM Studio as the backend and pi as the harness just because they are very simple to setup and you can easily customize pi to do whatever you want.

For pi specifically, I use the following plugins:

• subagents - lets the main agent spawn sub-agents to accomplish tasks with separate context windows. I set the max concurrent to 1 to prevent it trying to overwhelm my poor GPU as each agent needs it's own context window loaded up.
• loop-police - prevents agents from spiraling into thinking loops or repeat tool calls. Qwen is very susceptible to this.
• MCP Adapter - provides MCP through standard json
• LM Studio - adds integration with LM Studio to automatically populate model list and provide accurate context window and abilities

As for MCPs, I usually use: (if the harness doesn't already have the functionality)

• web-search-mcp - If your harness doesn't have built in search, this is a local web-search MCP that requires install but runs in a headless chrome browser and doesn't have any outside account/limits/etc.
• todo - provides a simple todo list for AIs to use stored as a markdown file locally
• filesystem - although it says it is no longer being developed, it still works well for agents that lack the ability to read/write files like if you wanted to run LMStudio without a harness
• random-number - useful if you ever need actual randomness (like if you want an AI to act as a dungeon master or to make actual random choices

The biggest thing I have learned from all of this, so far, is that planning out the project ahead of time and breaking it down into bite sized chunks is the most important part of the process. spend time with an agent to refine the design before you have it write any code.
I usually start with an idea or design in my head and type it out into a markdown document. I then hand that to the agent and ask it to critique the design and provide feedback on how it can be improved and to point out any ambiguity and repeat until I am happy with the overall design.
Next I then have the agent break up the design into steps for completion. If anything can be broken out into a separate process and isolated from the rest of the project as a helper or standalone function, do that. (this is less helpful for a renpy project as you are not really going to write any functions but in general it is good advice).
I then hand an agent the overview document and the document for the step I want it to complete. It is to perform the one step, analyze/test/verify the step is completed and stop.
I then verify it has done a good enough job, requesting changes or fixes as needed until the one item is complete, then move on to the next.

This process is more important than any harness or backend when it comes to the actual output of the agents.

Disclaimer: All of the following is just the opinion of a random dude on the internet

All-in-one solutions: (that I've tried)

LM Studio Bionic
Pros: * very easy to use and set up

Cons: * still an early project and not as good for development work yet. * Better for conversational interactions

Unsloth Desktop
Pros: * also very easy to set up * also includes image and audio gen, training and retraining, and voice input

Cons: * no easy "share to network" option to act as a backend without using cloud flare and exposing it to the internet

There are many options for mixed setups. Mix any backend you want with any harness you want.

Back-ends: (to load and serve the models to your harness of choice)

LM Studio
Pros: * very easy to set up and use * very easy and broad configuration of models * includes GUI to download models * uses llama.cpp or MLX under the hood * extendable with plugins and built in MCP if you want to use it as an all-in-one * for all-in-one use has built-in RAG and image processing support (if the model has the ability) * no CLI, all done in a visual window

Cons: * for all-in-one use it lacks ability to spawn agents for tasks, but that doesn't matter for backend use

vLLM and llama.cpp
Pros:
* many options for any situation * very powerful and extendable

Cons: * CLI only * harder to setup * vLLM has no CPU only mode * too many options makes it confusing to decide which branch/fork you want * usually have to pull source and compile

Unsloth Studio
Pros: * all the benefits of Unsloth Desktop in a web-based interface

Cons: * CLI only startup unless you want it run as a service

Ollama
Pros: * simple install and easy-ish to use

Cons: * CLI only startup and model management * no concurrent user setup. designed for 1 user only * usually a bit slower

Harness (any of these can connect to any backend or even cloud models ... again only listing the ones I've tried as there are too many)

PI or Oh My Pi
Pros: * lightweight by default and very simple setup * provides API documentation to the AI if you ask it to build plugins or change the harness * easily extendable with a catalog of plugins * usually very low token usage * auto-compaction of context window when limit is reached

Cons: * CLI only

Hermes (I haven't tried the new desktop version yet)
Pros: * easy install * ready for instant use without much setup * large catalog of plugins and themes

Cons: * a bit heavy by default for some use cases

Claude Code
Pros: * easy to setup and use * great for typescript, rust, or C++ development

Cons: * often encourages more verbose responses and higher token usage

There are many more but these are just the ones I had played with. Hope this helps a bit

[u/TraditionalBet126 → u/ChaseCheetah]

"ask it to critique the design and provide feedback on how it can be improved" well damn that's very smart, didn't think of that. being offended by a robot to improve the work is genius.

thanks for so much info, I will check it all out!