Qwen3.8-27B-NVFP4 is an open-weight model from Unsloth AI, released under Apache License 2.0. It has 19.9B parameters and a 262,144-token context. At 16-bit it needs about 47.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 1.9M downloads a month.
Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.
Runs On
What it takes to serve Qwen3.8-27B-NVFP4 (19.9B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 39.7 GB | 47.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 19.9 GB | 23.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 9.9 GB | 11.9 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 14, 2026.
Qwen3.8-27B-NVFP4 on every accelerator the SAVRN Index prices, at every precision
SAVRN's Notes on Qwen3.8-27B-NVFP4
Unsloth's NVFP4 build of Qwen/Qwen3.8-27B: 23.4 GB on disk across 13 files, a listing that counts 19.9B parameters, and a name that says 27B — reconcile those before you benchmark, and read the base listing this was quantized from. The memory table tops out at 47.7 GB for 16-bit, 23.8 GB for 8-bit, 11.9 GB for 4-bit, so one MI300X at $1.85 an hour holds every precision with the 262,144-token context beside it.
Apache 2.0 permits commercial use and modification with notices kept and changes stated. The base is a dense vision-language model that reads images and video, aimed at coding, research and long agentic work, so this build suits operators who want that stack at NVFP4 disk cost. No Index host serves it per token — the hourly rate is the only price — and since quantization is the point, validate quality against the BF16 base yourself.
Model Card
By Unsloth AI, published under apache-2.0, revision f0b7c9e722f5.
[!Note] This model works with vLLM. It also works with SGLang, but only with the v0.5.19, because the
lm_headis FP8 rather than 4-bit.
Read our How to Run Qwen3.8-27B Guide!
This NVFP4 quant uses Unsloth Dynamic V3.0 (preview) for SOTA quantization performance.
- Developer Role Support so Qwen3.8 can work in agentic tools like Codex and more!
- MTP for fast inference is available.
- Qwen3.8 can now be run and fine-tuned in Unsloth Desktop. Read our guide.
- Tool calling improvements: Makes parsing nested objects to make tool calling succeed more.
- See below for 4-bit Qwen3.8-27B run inside of Unsloth Desktop:
Qwen3.8-27B
Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.
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
- compressed-tensors
Identity and Version
- Repository
- unsloth/Qwen3.8-27B-NVFP4
- Publisher
- Unsloth AI
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 19.9B parameters
- Languages
- Not stated by the source
- Revision
- f0b7c9e722f5565102fff8481c99e4d86ae099c7
- First published
- 2026-08-13
- Last updated
- 2026-09-08
Files and Weights
13 files, 23.4 GB in total. The weights are 2 files totalling 23.4 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 22.6 GB | c473512c70ea |
| model_mtp.safetensors | Weights | 849.4 MB | 1d8268aa85ac |
| config.json | Configuration | 22.6 KB | — |
| generation_config.json | Configuration | 214 B | — |
| model.safetensors.index.json | Configuration | 164.4 KB | — |
| preprocessor_config.json | Configuration | 390 B | — |
| video_preprocessor_config.json | Configuration | 385 B | — |
| README.md | Documentation | 6.7 KB | — |
| chat_template.jinja | Other | 10.0 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 20.0 MB | 06b9509352d2 |
| tokenizer_config.json | Tokenizer | 1.0 KB | — |
| vocab.json | Tokenizer | 6.7 MB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 23.4 GB
Released by Unsloth AI through its official repository on Hugging Face. Read the license.
Built From
- Derived from Qwen/Qwen3.8-27B
- Quantized from Qwen/Qwen3.8-27B
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 23.4 GB |
| 16-bit | 39.7 GB |
| 8-bit | 19.9 GB |
| 4-bit | 9.9 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Qwen3.8-27B-NVFP4
How much GPU memory does Qwen3.8-27B-NVFP4 need?
About 47.7 GB at 16-bit and 11.9 GB at 4-bit: the weights (19.9B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Qwen3.8-27B-NVFP4 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.
Can I use Qwen3.8-27B-NVFP4 commercially?
Yes. Qwen3.8-27B-NVFP4 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is Qwen3.8-27B-NVFP4's context length?
262,144 tokens, from the maximum position embeddings in its published configuration.