Qwen3.8 Powers a Villager Simulation on 16GB VRAM
π‘See a full browser game built locally with a quantized Qwen3.8 model and only 16GB of VRAM.
β‘ 30-Second TL;DR
What Changed
The game was built incrementally with Qwen3.8-27B on a 16 GB RTX 5070 Ti, without using Cursor.
Why It Matters
The project provides a practical example of using a quantized local LLM as an iterative software-building partner, rather than judging it through a single prompt. It also suggests that faster generation with lower quantization may be more productive than a higher-quality model that triggers slow CPU offloading.
What To Do Next
Prototype a small coding-agent project with Qwen3.8-27B-UD-Q3_K_XL.gguf in llama.cpp, comparing Kvarn2/Kvarn3 KV caches against CPU-offloaded higher-precision variants.
Key Points
- β’The game was built incrementally with Qwen3.8-27B on a 16 GB RTX 5070 Ti, without using Cursor.
- β’The setup reaches up to 75 tokens per second for generation and 1,700 tokens per second for prompt processing.
- β’Q3 model quantization and Kvarn KV quantization kept the model fully on the GPU instead of forcing CPU offload.
- β’Kvarn2 was used for the MTP draft cache, providing high acceptance rates while reducing VRAM usage.
- β’The prototype includes maps, minimaps, resource logistics, housing, weather, seasons, day-night cycles, and villager obstacle avoidance.
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Original source: Reddit r/LocalLLaMA β
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