Local LLM Moon Landing Simulation C# Co-Development Performance Results using my Alienware Aurora R11 RTX-3080 10GB using LM Studio
For the past several months, I have been co-developing a Moon Landing simulation game using several different open source LLM models running “locally” on my Alienware Aurora 11 GeForce RTX-3080 10GB VRAM video card. Running LLMs locally using my RTX 3080 is completely free compared to using cloud based LLMs.
In my last blog post I shared the performance results using the Open Source Ollama local LLM management system that runs under a “text-based” command window. In this blog post, I will share the C# Moon Landing Simulation C# Co-Development performance results using the GUI based LM Studio application. LM Studio is not open source but is free for individuals to use and can be licensed for companies or schools to use.
In my personal coding tests, I loaded the google/gemma-3-12b model locally into the GeForce RTX-3080 10GB of VRAM on my Alienware Aurora 11 PC which also has 64GB of system RAM.
I tested building a C# Lunar Lander simulation game using the open source google/gemma-3-12b available from their website to see how the performance results compared to the Lunar Lander game that I previously built using the “text-based” Ollama software.
Building the Lunar Lander simulation game with LM Studio using google/gemma-3-12b came in at 8.45 tokens per second:
Building the Lunar Lander simulation game using deepseek-r1-0528-qwen3-8b with LM Studio came in at 74.94 tokens per second:
As you can see below my GeForce RTX-3080 got pretty hot running these tests. Fortunately, Dell designed the Alienware Aurora 11 to handle this kind of GPU computing heat!
I also tried loading a larger LLM like Meta’s llama-3.3-70b (70 billion parameters) using LM Studio by running part of the model on the RTX-3080 10GB VRAM while offloading rest of the model into the 64GB of slower System RAM on my Alienware Aurora 11.
LM Studio did load the model, but I only got 0.67 Tokens per second which was totally useless. I actually canceled the Moon Landing C# build as it was pegging all my resources and was taking way too long to actually code the Lunar Lander C# program.
I definitely need a Nvidia GeForce GPU with a lot more memory (like the RTX-5090) or an AI specific CPU/APU/NPU with at least 96GB of Integrated RAM to use the larger llama-3.3-70b “70 billion” parameter model on a consumer development PC or laptop!
I’ll either need to upgrade to an Apple MAC M4 with 96GB of shared memory or an AMD Ryzen AI Max + 395 laptop like the new Asus Flow Z13 with 128GB of Integrated memory to run larger models like this. On the other hand, it may be best to wait until the rumored ARM based MediaTek – Nvidia SOC N1X based 128GB laptops hit the market in 2026.
The new Nvidia DGX Spark workstation, based on the Nvidia GB10 Superchip, can definitely handle these large models but it is a little too expensive for me right now and I want to be able to still play games on my AI development system.
Until then, I will continue my AI Co-Development testing throughout the summer and let you know what my conclusions are by the time I start teaching my next Unreal Engine course in-person at Northwest University this Fall.
By the way, here is the list of DeepSeek, Google, Meta and Quen LLMs that I tested locally using LM Studio (sorted by size):
My next set of blog posts will be dedicated to using various AI tools and LLMs within popular Game Engines like Unity, Unreal Engine and GoDot.
BookAuthority Selects Philip Conrod and Lou Tylee’s 11th Edition Java Swing Tutorials As the Best New Java Swing Books Again to Read in 2021! BookAuthority also named several of our Java Swing textbooks as the “
