How much memory do I need to run Qwen3.8-27B-Heretic-GSQ-RCO (IQ2_XS)?

The file size is 8.45 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 10.2 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.

Does Qwen3.8-27B-Heretic-GSQ-RCO run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?

8 GB: no quantization fits; 12 GB: up to IQ3_XXS (est. 12.3 GB); 16 GB: up to IQ3_S (est. 14.0 GB); 24 GB: up to IQ3_S (est. 14.0 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).

What is the command to run Qwen3.8-27B-Heretic-GSQ-RCO with Ollama?

Run "ollama run hf.co/0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF:IQ2_XS" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.

0bserverx

Qwen3.8-27B-Heretic-GSQ-RCO

Parameters: 26.9B
Max context length: 262,144
Architecture: qwen35
License: Commercial OK

Base model: 0bserverx/Qwen3.8-27B-Heretic-Abliterated-Uncensored
License: apache-2.0
llama.cpp: b7990+

Memory needed to run IQ2_XS at 8K tokens

10.2 GB Estimated

File size 8.45 GB Measured

VRAM it fits in (rough guide)

8 GB does not fit
12 GB fits
16 GB fits
24 GB fits
32 GB fits
48 GB fits

See devices and models for 12 GB

Macs cap how much memory the GPU can use, so leave headroom in unified memory.

Data as of Nov 11, 2026

About Qwen3.8-27B-Heretic-GSQ-RCO

Qwen3.8-27B-Heretic-GSQ-RCO is a GGUF model based on 0bserverx/Qwen3.8-27B-Heretic-Abliterated-Uncensored on Hugging Face. GGUF files are published by 0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF. It has about 26.9B parameters, the qwen35 architecture, a maximum context length of 262,144 tokens (values written in the GGUF).

There are 4 quantizations, with file sizes from 8.45 GB (IQ2_XS) to 12.2 GB (IQ3_S) (actual sizes on Hugging Face).

Running IQ2_XS (8.45 GB) with an 8,192-token context needs an estimated 10.2 GB of memory, and an estimated 11.8 GB at 32,768 tokens. For 8,192 tokens, a GPU with 12 GB or more of VRAM (such as GeForce RTX 3080 Ti, GeForce RTX 5070), or a Mac with 12 GB or more of unified memory, is a rough guide (macOS caps how much memory the GPU can use, so leave some headroom).

The original model is licensed under Apache License 2.0, which allows commercial use (keep the license text and copyright notice when redistributing). Check the original license for the terms of use.

File size and estimated memory per quantization

Up to 8 GB
Up to 16 GB
Up to 24 GB
Up to 48 GB
Up to 128 GB
More

File sizes are the actual values on Hugging Face. Memory is estimated for each context length (KV cache f16). The formula is in How memory is calculated.

All memory figures are Estimated (KV cache f16). It should run on a setup with at least this much VRAM (or Mac unified memory). Click a value to see the devices and models that fit that amount (Find by GPU / Mac).

About the "quality guide" (how far a quantization drifts from the original)

For each quantization type, how far the output drifts from the original model, grouped into six levels from values llama.cpp measured on Llama-3-8B.

The value is mean KLD (the difference between the original and quantized model in next-token probabilities); smaller means closer to the original. Quantizations made with an imatrix (importance data) come out slightly smaller. Levels use the value without an imatrix (or with one, for types that only have that).

These values were not measured on this model. Larger models and MoE models tend to drift less with the same type. Distributor-specific mixed types (such as UD-Q4_K_XL) cannot be matched by type name alone, so they show "-".

Quantization	Quality guide	KLD (no imatrix)	KLD (imatrix)	Size (Llama-3-8B)
Q8_0	Nearly identical	0.0014	-	7.96 GiB
Q6_K	Very small	0.0055	-	6.14 GiB
Q5_K_M	Very small	0.0108	-	5.33 GiB
Q5_K_S	Small	0.0166	-	5.21 GiB
Q5_1	Small	0.0180	-	5.65 GiB
Q5_0	Small	0.0222	-	5.21 GiB
Q4_K_M	Small	0.0313	0.0282	4.58 GiB
Q4_K_S	Medium	0.0431	0.0320	4.37 GiB
IQ4_NL	Small	-	0.0357	4.35 GiB
IQ4_XS	Small	-	0.0363	4.14 GiB
Q4_1	Medium	0.0717	-	4.78 GiB
Q4_0	Medium	0.0719	-	4.34 GiB
Q3_K_L	Medium	0.0872	0.0731	4.03 GiB
Q3_K_M	Medium	0.1019	0.0844	3.74 GiB
IQ3_M	Medium	-	0.1025	3.53 GiB
IQ3_S	Medium	-	0.1113	3.42 GiB
IQ3_XS	Large	-	0.1387	3.28 GiB
IQ3_XXS	Large	-	0.1836	3.05 GiB
Q3_K_S	Large	0.2282	0.1998	3.41 GiB
IQ2_M	Large	-	0.3260	2.74 GiB
Q2_K	Very large	0.4451	0.3322	2.96 GiB
Q2_K_S	Very large	-	0.4034	2.96 GiB
IQ2_S	Very large	-	0.4392	2.56 GiB
IQ2_XS	Very large	-	0.5463	2.43 GiB
IQ2_XXS	Very large	-	0.8120	2.24 GiB
IQ1_M	Very large	-	1.3931	2.01 GiB
IQ1_S	Very large	-	2.2546	1.88 GiB

Source: llama.cpp (MIT), measured on Llama-3-8B against FP16, Wikitext-2, revision f364eb6f. Original table · Reading and choosing quantizations

GGUF source

0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF

Published by the original developer Modified version (name includes abliterated etc.) About this label

Last checked: Nov 11, 2026

Architecture: qwen35; llama.cpp b7990 and later
Parameters: 26.9B
Max context length: 262,144
License: apache-2.0 Commercial OK

File size and memory per quantization for Qwen3.8-27B-Heretic-GSQ-RCO (0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF)
Quantization	File size Measured	Quality guide	4,096	8,192	16,384	32,768	65,536	131,072	262,144 max
IQ2_XS RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ2_XS	8.45 GB	Very large	9.92 GB	10.2 GB	10.7 GB	11.8 GB	13.9 GB	18.2 GB	26.8 GB
IQ2_XS RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ2_XS-mtp	8.90 GB	Very large	10.4 GB	10.6 GB	11.2 GB	12.2 GB	14.4 GB	18.7 GB	27.3 GB
IQ2_S RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ2_S	9.28 GB	Very large	10.8 GB	11.0 GB	11.6 GB	12.6 GB	14.8 GB	19.1 GB	27.7 GB
IQ2_S RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ2_S-mtp	9.73 GB	Very large	11.2 GB	11.5 GB	12.0 GB	13.1 GB	15.2 GB	19.5 GB	28.1 GB
IQ3_XXS RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ3_XXS	10.1 GB	Large	11.6 GB	11.9 GB	12.4 GB	13.5 GB	15.6 GB	19.9 GB	28.5 GB
IQ3_XXS RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ3_XXS-mtp	10.6 GB	Large	12.0 GB	12.3 GB	12.8 GB	13.9 GB	16.1 GB	20.4 GB	29.0 GB
IQ3_S RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ3_S	11.8 GB	Medium	13.3 GB	13.5 GB	14.1 GB	15.1 GB	17.3 GB	21.6 GB	30.2 GB
IQ3_S RVN-Qwen3.8-27B-Heretic-GSQ-RCO-IQ3_S-mtp	12.2 GB	Medium	13.7 GB	14.0 GB	14.5 GB	15.6 GB	17.7 GB	22.0 GB	30.6 GB

Ollama  ollama run hf.co/0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF:IQ2_XS

llama.cpp  llama-server -hf 0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF:IQ2_XS

Generation speed on common GPUs and Macs (estimated)

A rough guide to how fast IQ2_XS (8.45 GB) generates text. Each token reads the model weights once, so the upper limit is memory bandwidth ÷ file size. The guide is 60–80% of that, where most published measurements fall.

Device	Memory bandwidth	Guide (tokens/s) Estimated	Upper limit
Radeon RX 7600 XT 16 GB	288 GB/s	20–27	34
Radeon RX 6950 XT 16 GB	576 GB/s	41–55	68
GeForce RTX 5090 32 GB	1,792 GB/s	127–170	212
Apple M2 16 GB	100 GB/s	7.1–9.5	12
Apple M2 Pro 16 GB	200 GB/s	14–19	24
Apple M5 Ultra 96 GB	1,200 GB/s	85–114	142

Estimates only. Prompt processing speed (limited by compute) is not included. Longer contexts and offloading part of the model to the CPU make it slower. Macs are shown only when installed memory is at least 4/3 of what is needed. Devices without published bandwidth (some GeForce cards, etc.) are not shown.

License and commercial use

Apache License 2.0 Commercial OK

Allows commercial use, modification and redistribution. When redistributing, keep the license text and copyright notice, and note changes in modified files.

Based on the license on the original model's Hugging Face page. Original license text (checked Oct 18, 2026)

The summary states facts this site read from the original text and is not legal advice. Always check the original license and its latest terms before use. All licenses

How to run

Mainline llama.cpp can load this model's format (qwen35) from b7990 (Feb 10, 2026) onward. Ollama and LM Studio bundle llama.cpp internally, so an older app fails to load it with errors such as "unknown model architecture". If that happens, update the app.

Example commands using IQ2_XS. To use another quantization, replace the name with one from the table above. How to run local LLMs

Ollama

Run it by pointing directly at the GGUF on Hugging Face.

ollama run hf.co/0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF:IQ2_XS

llama.cpp

Pass the repository and quantization name to llama-server (or llama-cli) with -hf.

llama-server -hf 0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF:IQ2_XS

LM Studio

Paste 0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF into the search box, download it, and choose the quantization from the file list.

0bserverx

Qwen3.8-27B-Heretic-Abliterated-Uncensored

26.9B

Q4_K_M size

16.5 GB

Memory at 8K

18.3 GB

Measured Estimated VRAM 24 GB+

Frequently asked questions

How much memory do I need to run Qwen3.8-27B-Heretic-GSQ-RCO (IQ2_XS)?: The file size is 8.45 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 10.2 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can Qwen3.8-27B-Heretic-GSQ-RCO be used commercially?: The original model's license is Apache License 2.0 (Commercial use allowed).Allows commercial use, modification and redistribution. When redistributing, keep the license text and copyright notice, and note changes in modified files. Check the original license text before deciding.
Does Qwen3.8-27B-Heretic-GSQ-RCO run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?: 8 GB: no quantization fits; 12 GB: up to IQ3_XXS (est. 12.3 GB); 16 GB: up to IQ3_S (est. 14.0 GB); 24 GB: up to IQ3_S (est. 14.0 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
What is the command to run Qwen3.8-27B-Heretic-GSQ-RCO with Ollama?: Run "ollama run hf.co/0bserverx/Qwen3.8-27B-Heretic-GSQ-RCO-GGUF:IQ2_XS" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.