Unsloth's Q5_K_XL quant of Gemma 4 12B becomes local coding favorite
50 t/s with 8.6GB model file and fewer syntax errors
A Reddit user has declared the Unsloth Q5_K_XL quantization of Gemma 4 12B as their new main squeeze for local coding. After initially using the Q4 variant at 61 tokens per second, they were frustrated by frequent syntax errors—once needing 23 edits on a single file. Switching to the Q5_ K_XL dropped speed to 50 t/s but dramatically improved reliability, making most coding tasks one-shot. The model file sits at 8.6GB, and with a 32k context window and Q8 KV cache in llama.cpp, total VRAM usage reaches about 15.7GB, with a gigabyte spilling over to cached checkpoints.
The user compares Gemma 4 favorably against Qwen 3.6 27B (which they couldn't run due to size) and a 35B A3B variant. The main differentiator is tool call simplicity: Qwen required converting XML tool calls to JSON and extensive tweaking of chat templates, llama.cpp settings, and memory management. Gemma 4, by contrast, is plug-and-play—just set the cache, lock the context length, and attach it to a personal inference harness. The model excels at writing code, short stories, HTML games, and even Lua scripts for Cyberpunk 2077 modding. The user still plans to test it with Godot, but overall considers it a worthy upgrade that justifies the small speed sacrifice.
- Unsloth Q5_K_XL quantization of Gemma 4 12B delivers 50 t/s with minimal syntax errors, enabling one-shot code generation.
- Model uses ~8.6GB disk, ~15.7GB VRAM with 32k context and Q8 KV cache in llama.cpp.
- Preferred over Qwen 3.6 27B due to plug-and-play tool calls requiring no special configuration.
Why It Matters
Local developers can now run a reliable 12B model with easy setup, drastically reducing debugging time for coding tasks.