Using GLM 4.6V Flash Local LLM Model by Z.ai to Develop a Moon Landing Simulation Using C# on my Alienware Aurora R11 RTX-3080 10GB Video Card
Over the past 6 months, I have continued to test locally hosted open-source multimodal agentic models which could run comfortably on my Nvidia RTX 3080 with 10GB. Back in the August, I added GLM 4.5 to my testbench as it was surpassing or matching DeepSeek V3, Qwen 2.5 Coder, and Llama 3.1 in benchmarks. At the time, I was busy testing OpenAI GPT OSS so I didn’t write a blog post about my GLM testing.
Z.ai recently launched GLM 4.6V Flash which is a 9B vision-language model optimized for local deployment and low-latency applications. It supports a context length of 128k tokens and achieves strong performance in visual understanding among models of similar scale. The model introduces native multimodal function calling, enabling vision-driven tool use where images, screenshots, and document pages can be passed directly as tool inputs without text conversion.
It needs a “minimum” of 8GB VRAM to run so I ran my standard Lunar Lander Simulation coding test using the default 4096 token context window. My initial coding test quickly reached the LM Studio default 4086 context window and errored out with a “Failed to send message” after 1 minute and 44 seconds:
I then reloaded the model with a maximum token context window of 131072 with maximum layers offloaded to the GPU:
The Lunar Lander coding simulation coding test then ran without any problems at 11.69 tokens per second:
Below you can see the Task Manager Performance Chart for my NVIDIA GeForce RTX 3080;
The Open Source LLM Models keep getting better and better at coding with each new release. I’ll continue testing the highest rated open-source coding models against the cloud versions to see which ones are best for AI assisted coding work.
Wishing you and your family a Merry Christmas and a Happy New Year!
