Qwen3.6 27B pure quant fits 16GB VRAM, hits 40 tok/s
Pure Q4_K_M GGUF cuts model size to 15.1 GB, runs locally
A new pure quantization method for Qwen3.6-27B now allows the 27-billion-parameter model to run entirely on consumer GPUs with 16GB VRAM, such as the RTX 5060 Ti. Developer huytd189, building on earlier work by Due-Project-7507, released two GGUF variants using the Q4_K_M scheme: a 15.4GB Multi-Token Prediction (MTP) version and a 15.1GB non-MTP version. The MTP variant achieves 40 tokens per second (tok/s) during text generation and 195 tok/s during prompt processing, while the non-MTP variant offers 24 tok/s generation but faster prompt processing at 715 tok/s.
Compared to other popular quants from unsloth or mradermacher that require 16.5–18GB, huytd189's pure quantization saves 1–3GB with only a slight increase in perplexity (+0.1707 for MTP, +0.1051 for non-MTP over the BF16 baseline). The weights are available on Hugging Face and can be run with llama.cpp using provided server commands. This breakthrough makes large-scale local LMs practical for developers and researchers with mid-range GPUs, enabling private, cost-effective deployment of advanced AI assistants.
- Pure Q4_K_M reduces model size to 15.1 GB (non-MTP) — 1–3 GB smaller than existing quants
- MTP version: 40 tok/s generation; non-MTP: 24 tok/s but 715 tok/s prompt processing
- Perplexity loss is minimal: +0.17 (MTP) and +0.10 (non-MTP) vs BF16 baseline
Why It Matters
Enables local 27B-parameter AI on 16GB GPUs, making high-quality models accessible without cloud costs.