Llamacpp imatrix Quantizations of gpt-oss-20b by openai

Using llama.cpp release b6096 for quantization.

Original model: https://huggingface.co/openai/gpt-oss-20b

All quants made using imatrix option with combinedallmedium dataset from Ed Addario here

Run them in LM Studio

Run them directly with llama.cpp, or any other llama.cpp based project

All quants keep the feed forward networks at mxfp4 for optimal performance, which does mean the size differences are negligible unfortunately, but being provided just because.

Prompt format

No chat template specified so default is used. This may be incorrect, check original model card for details.

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-05

Reasoning: medium

Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>developer<|message|># Instructions

{system_prompt}<|end|><|start|>user<|message|>{prompt}<|end|><|start|>assistant

Download a file (not the whole branch) from below:

Use this one:

The rest of these are provided for your own interest in case you feel like experimenting, but the size savings is basically non-existent so I would not recommend running them, they are provided simply for show:

Embed/output weights

Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/openaigpt-oss-20b-GGUF --include "openaigpt-oss-20b-Q4KM.gguf" --local-dir ./

huggingface-cli download bartowski/openaigpt-oss-20b-GGUF --include "openaigpt-oss-20b-Q8_0/*" --local-dir ./

ARM/AVX information

Previously, you would download Q4044/48/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q40 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q40XX files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.