# Qwen 27B: Which Quant Should You Run? **URL:** \ **Category:** Content Creators\ **Tags:** the-stack, machine-learning, coding, quantisation, running-locally\ **Created:** [3 October 2026 19:00 UTC](https://www.artofsm.art/t/qwen-27b-which-quant-should-you-run/24961 "2026-10-03T19:00:14Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [3 October 2026 19:00 UTC](https://www.artofsm.art/t/qwen-27b-which-quant-should-you-run/24961/1 "2026-10-03T19:00:14Z")
[![](https://www.artofsm.art/uploads/default/original/3X/7/f/7f969f9ae43d8b21dab2075f2c1c4bc8a4d9090a.jpeg "Qwen 27B: Which Quant Should You Run?") ](https://www.youtube.com/watch?v=zxX0Vl3zwKQ) The video recommends starting with the Q4K MQAT quant for running the Qwen 27B model on a 24 GB GPU due to its lower memory usage and sufficient coding performance, while suggesting users conduct controlled tests to determine if upgrading to the higher-precision Q6K quant justifies the increased memory demands. It emphasizes that improved numerical fidelity does not guarantee better coding outcomes, and the best quant choice depends on individual hardware, coding tasks, and workflow considerations. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [3 October 2026 19:20 UTC](https://www.artofsm.art/t/qwen-27b-which-quant-should-you-run/24961/3 "2026-10-03T19:20:31Z")
The video discusses running the Qwen 27B model locally for coding tasks on a 24 GB GPU, focusing on which quantized version to choose. The recommended starting point is the Q4K MQAT quant, which uses less memory and leaves more headroom for actual coding work compared to the higher precision Q6K quant. Although Q6K offers better numerical fidelity to the original model, it demands nearly the full 24 GB of GPU memory, making it a tighter fit and potentially less practical depending on your runtime setup. The file size on disk does not directly translate to memory usage during execution, as additional memory is needed for context, instructions, and cached computations. The video explains that while Q6K better matches the original model’s text predictions, this does not necessarily mean it produces better or more reliable code. The fidelity improvements reflect closer alignment with the original model’s probabilities but do not guarantee successful code compilation or passing unit tests. Moreover, the success of tool commands during coding depends heavily on the host application’s ability to parse and execute the model’s requests, not just on the quantization level. Troubleshooting failed tool calls involves checking whether the host application correctly processed the model’s commands and returned the expected results. To decide whether upgrading from Q4 to Q6 is worthwhile, the video recommends running controlled, side-by-side tests on your own codebase. This involves preparing two contrasting coding tasks—a focused bug fix and a broader multi-file refactor—and running both quant versions under identical conditions. Key metrics to log include test pass rates, tool execution success, elapsed time, peak memory usage, and manual interventions needed. Repeating these tests multiple times helps avoid false positives and provides a realistic assessment of each quant’s practical coding performance on your hardware. If Q6 fits comfortably in GPU memory and consistently delivers better results with fewer manual fixes and reasonable speed, it justifies the extra memory cost. However, if Q6 requires offloading parts of the model to system memory, this changes the execution dynamics and may slow down the workflow. In such cases, measuring the trade-offs between code quality improvements and performance impacts on your specific setup is crucial. The video emphasizes that no definitive winner exists without these personalized tests, as the best quant depends on your hardware, coding tasks, and workflow requirements. In conclusion, for users with a 24 GB GPU, the video advises starting with the Q4KM quant due to its lower memory footprint and greater headroom for practical coding sessions. Only consider upgrading to Q6K after validating its benefits through rigorous, controlled testing on your own projects. This approach ensures that any increase in precision translates into tangible improvements in coding productivity without compromising system stability or speed. The video encourages viewers to subscribe and engage for more insights on running large language models locally. ## Useful Links - [Qwen3.8-27B GGUF quants by bartowski (Hugging Face)](https://huggingface.co/bartowski/Qwen3.8-27B-GGUF) — Direct source of the quantized model files discussed and compared in the video. - [Qwen3.8-27B official model page (Hugging Face)](https://huggingface.co/Qwen/Qwen3.8-27B) — Official source of the base Qwen3.8-27B model underlying the quantized variants compared in the video. - [LM Studio documentation: lms load (–estimate-only)](https://lmstudio.ai/docs/cli/local-models/load) — Explains how to estimate GPU memory usage for local LLMs, a key step in deciding which quant to run. - [LM Studio documentation: tool use](https://lmstudio.ai/docs/developer/openai-compat/tools) — Provides official guidance on tool call execution and troubleshooting in LM Studio, relevant to evaluating quant performance with tools. - [llama.cpp documentation: function calling](https://github.com/ggml-org/llama.cpp/blob/master/docs/function-calling.md) — Official documentation on function calling in llama.cpp, relevant to understanding tool call mechanics discussed in the video.