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Open-weight model · Image and text to text

Swift-1.5-Qwen3.8-27b-FP8

by Ethan Todd ethantodd4l/Swift-1.5-Qwen3.8-27b-FP8

Swift-1.5-Qwen3.8-27b-FP8 is an open-weight model for image and text to text from Ethan Todd, released under other. It has 27.8B parameters and a 262,144-token context. At 16-bit it needs about 66.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 31 downloads a month.

quantized to FP8 with the same recipe and checkpoint format as the official Qwen/Qwen3.8-27B-FP8: weights in FP8 E4M3 with 128x128 block scales, activations dynamically quantized (activationscheme: dynamic), serialized in the compressed-tensors/ quantmethod…

Parameters27.8B
Context262,144
Weights30.9 GB
Licenseother
AccessOpen weights
Monthly Downloads31

Runs On

What it takes to serve Swift-1.5-Qwen3.8-27b-FP8 (27.8B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 55.6 GB 66.7 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 27.8 GB 33.3 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 13.9 GB 16.7 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 7, 2026.

Swift-1.5-Qwen3.8-27b-FP8 on every accelerator the SAVRN Index prices, at every precision

Model Card

quantized to FP8 with the same recipe and checkpoint format as the official Qwen/Qwen3.8-27B-FP8: weights in FP8 E4M3 with 128x128 block scales, activations dynamically quantized (activationscheme: dynamic), serialized in the compressed-tensors/ quantmethod: fp8 layout that vLLM loads directly. modulestonotconvert is a superset of the reference checkpoint's list (1111 vs 882 entries): every module that is BF16 in this checkpoint is listed, and the quantized tensor set is exactly the reference's. Tensor inventory matched against Qwen/Qwen3.8-27B-FP8: identical 1606 keys (407 F8E4M3 + 1199 BF16), identical quantized module set, no name or shape differences. Served with vLLM Radiance (vLLM…

Excerpt from the card by Ethan Todd, licensed other.

Configuration

Architecture
Qwen3_5ForConditionalGeneration
Context length (tokens)
262,144
Layers
64
Hidden size
5,120
Feed-forward size
17,408
Attention heads
24
Key/value heads
4
Head dimension
256
Vocabulary size
248,320
Model type
qwen3_5
Quantization
fp8

Identity and Version

Repository
ethantodd4l/Swift-1.5-Qwen3.8-27b-FP8
Publisher
Ethan Todd
Task
Image and text to text
Modality
Image and text
Library
transformers
Parameters
27.8B parameters
Languages
Not stated by the source
Revision
7c187a50be6a25b5246a8f4004c0bb6579ce6e9e
First published
2026-10-02
Last updated
2026-10-04

Files and Weights

32 files, 30.9 GB in total. The weights are 18 files totalling 30.9 GB in safetensors.

Weights18 files · 30.9 GB
Configuration5 files · 218.5 KB
Tokenizer4 files · 22.9 MB
Documentation3 files · 19.0 KB
Other1 file · 9.0 KB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00018.safetensorsWeights2.4 GB 6b22163792ac
model-00002-of-00018.safetensorsWeights1.5 GB d445e2aa7c86
model-00003-of-00018.safetensorsWeights2.5 GB 0dbb0dbcff68
model-00004-of-00018.safetensorsWeights2.0 GB e8bff152789f
model-00005-of-00018.safetensorsWeights1.1 GB 512761ae9387
model-00006-of-00018.safetensorsWeights2.0 GB 8c66e0f7838c
model-00007-of-00018.safetensorsWeights1.1 GB 060217ff4035
model-00008-of-00018.safetensorsWeights2.0 GB eb124dcb28d1
model-00009-of-00018.safetensorsWeights1.1 GB ed60a65fcae7
model-00010-of-00018.safetensorsWeights2.0 GB ebeb9454eb72
model-00011-of-00018.safetensorsWeights1.1 GB 1a6da45691d6
model-00012-of-00018.safetensorsWeights2.0 GB eb2e18c49dfe
model-00013-of-00018.safetensorsWeights1.1 GB b37535de2559
model-00014-of-00018.safetensorsWeights2.0 GB 56306066e89d
model-00015-of-00018.safetensorsWeights1.1 GB 4088f6a7cfbd
model-00016-of-00018.safetensorsWeights2.0 GB e8db96466943
model-00017-of-00018.safetensorsWeights1.1 GB dae4cdca3b28
model-00018-of-00018.safetensorsWeights3.0 GB 20f7b5041d53
config.jsonConfiguration61.5 KB —
generation_config.jsonConfiguration202 B —
model.safetensors.index.jsonConfiguration156.0 KB —
preprocessor_config.jsonConfiguration390 B —
video_preprocessor_config.jsonConfiguration385 B —
LICENSEDocumentation13.3 KB —
NOTICEDocumentation1.2 KB —
README.mdDocumentation4.5 KB —
chat_template.jinjaOther9.0 KB —
.gitattributesRepository1.7 KB —
merges.txtTokenizer3.4 MB —
tokenizer.jsonTokenizer12.8 MB 0997f410c57a
tokenizer_config.jsonTokenizer17.9 KB —
vocab.jsonTokenizer6.7 MB —

License and Download

License
other
Access
Open weights, no gate
Download size
30.9 GB
Download from Ethan Todd

Released by Ethan Todd through its official repository on Hugging Face. Read the license.

Built From

  • Derived from ukisai/Swift-1.5-Qwen3.8-27b
  • Quantized from ukisai/Swift-1.5-Qwen3.8-27b

Memory Requirements

PrecisionWeights in memory
As published30.9 GB
16-bit55.6 GB
8-bit27.8 GB
4-bit13.9 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Swift-1.5-Qwen3.8-27b-FP8

How much GPU memory does Swift-1.5-Qwen3.8-27b-FP8 need?

About 66.7 GB at 16-bit and 16.7 GB at 4-bit: the weights (27.8B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Swift-1.5-Qwen3.8-27b-FP8 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

What license is Swift-1.5-Qwen3.8-27b-FP8 released under?

other, as its publisher declares it. Read the license text before commercial use.

What is Swift-1.5-Qwen3.8-27b-FP8's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

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