How much memory do I need to run Swift-1.5-Qwen3.8-27B-GSQ-RCO (IQ2_XS)?

The file size is 8.42 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 Swift-1.5-Qwen3.8-27B-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. 12.2 GB); 16 GB: up to IQ3_S (est. 13.9 GB); 24 GB: up to IQ3_S (est. 13.9 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).

What is the command to run Swift-1.5-Qwen3.8-27B-GSQ-RCO with Ollama?

Run "ollama run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_XS" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.

ukisai

Swift-1.5-Qwen3.8-27B-GSQ-RCO

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

Base model: ukisai/Swift-1.5-Qwen3.8-27b
License: swift-open-license-1.0
llama.cpp: b7990+

Memory needed to run IQ2_XS at 8K tokens

10.2 GB Estimated

File size 8.42 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 Swift-1.5-Qwen3.8-27B-GSQ-RCO

Swift-1.5-Qwen3.8-27B-GSQ-RCO is a GGUF model based on ukisai/Swift-1.5-Qwen3.8-27b on Hugging Face. GGUF files are published by ukisai/Swift-1.5-Qwen3.8-27B-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 8.42 GB (IQ2_XS) to 12.1 GB (IQ3_S) (actual sizes on Hugging Face).

Running IQ2_XS (8.42 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 Swift Open License v1.0, which allows commercial use with conditions such as scale or purpose. 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

ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF

Published by the original developer About this label

Last checked: Nov 11, 2026

Architecture: qwen35; llama.cpp b7990 and later
Parameters: 26.9B
Max context length: 262,144
License: swift-open-license-1.0 Conditional

File size and memory per quantization for Swift-1.5-Qwen3.8-27B-GSQ-RCO (ukisai/Swift-1.5-Qwen3.8-27B-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 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ2_XS	8.42 GB	Very large	9.89 GB	10.2 GB	10.7 GB	11.8 GB	13.9 GB	18.2 GB	26.8 GB
IQ2_XS Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ2_XS-mtp	8.77 GB	Very large	10.2 GB	10.5 GB	11.0 GB	12.1 GB	14.3 GB	18.6 GB	27.2 GB
IQ2_S Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ2_S	9.26 GB	Very large	10.7 GB	11.0 GB	11.5 GB	12.6 GB	14.8 GB	19.1 GB	27.6 GB
IQ2_S Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ2_S-mtp	9.61 GB	Very large	11.1 GB	11.3 GB	11.9 GB	13.0 GB	15.1 GB	19.4 GB	28.0 GB
IQ3_XXS Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS	10.1 GB	Large	11.6 GB	11.8 GB	12.4 GB	13.4 GB	15.6 GB	19.9 GB	28.5 GB
IQ3_XXS Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp	10.4 GB	Large	11.9 GB	12.2 GB	12.7 GB	13.8 GB	15.9 GB	20.2 GB	28.8 GB
IQ3_S Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S	11.8 GB	Medium	13.2 GB	13.5 GB	14.0 GB	15.1 GB	17.3 GB	21.6 GB	30.2 GB
IQ3_S Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp	12.1 GB	Medium	13.6 GB	13.9 GB	14.4 GB	15.5 GB	17.6 GB	21.9 GB	30.5 GB

Ollama  ollama run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_XS

llama.cpp  llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_XS

Generation speed on common GPUs and Macs (estimated)

A rough guide to how fast IQ2_XS (8.42 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	21–27	34
Radeon RX 6950 XT 16 GB	576 GB/s	41–55	68
GeForce RTX 5090 32 GB	1,792 GB/s	128–170	213
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

Swift Open License v1.0 Conditional

Commercial use by organizations with total revenue of USD 1 million or more in the most recent fiscal year is not permitted under this license (a separate agreement is needed). Below that, commercial use is allowed.

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/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_XS

llama.cpp

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

llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_XS

LM Studio

Paste ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF into the search box, download it, and choose the quantization from the file list.

Frequently asked questions

How much memory do I need to run Swift-1.5-Qwen3.8-27B-GSQ-RCO (IQ2_XS)?: The file size is 8.42 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 Swift-1.5-Qwen3.8-27B-GSQ-RCO be used commercially?: The original model's license is Swift Open License v1.0 (Commercial use with conditions).Commercial use by organizations with total revenue of USD 1 million or more in the most recent fiscal year is not permitted under this license (a separate agreement is needed). Below that, commercial use is allowed. Check the original license text before deciding.
Does Swift-1.5-Qwen3.8-27B-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. 12.2 GB); 16 GB: up to IQ3_S (est. 13.9 GB); 24 GB: up to IQ3_S (est. 13.9 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
What is the command to run Swift-1.5-Qwen3.8-27B-GSQ-RCO with Ollama?: Run "ollama run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_XS" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.