Qwen3.8-27B-AutoRound-NVFP4 is an open-weight model from Jl. It has 16.7B parameters and a 262,144-token context. At 16-bit it needs about 40.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Runs On
What it takes to serve Qwen3.8-27B-AutoRound-NVFP4 (16.7B 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 | 33.4 GB | 40.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 16.7 GB | 20.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 8.4 GB | 10.0 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 18, 2026.
Qwen3.8-27B-AutoRound-NVFP4 on every accelerator the SAVRN Index prices, at every precision
Model Card
The publisher has not written a card for this model.
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
- auto-round
Identity and Version
- Repository
- jia0160/Qwen3.8-27B-AutoRound-NVFP4
- Publisher
- Jl
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 16.7B parameters
- Languages
- Not stated by the source
- Revision
- 79e459d6c0a050eca0bb03dcdee68af38001663d
- First published
- 2026-10-14
- Last updated
- 2026-10-14
Files and Weights
18 files, 20.6 GB in total. The weights are 8 files totalling 20.6 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00007.safetensors | Weights | 3.2 GB | a79fe8c22214 |
| model-00002-of-00007.safetensors | Weights | 3.2 GB | 082fedd8a598 |
| model-00003-of-00007.safetensors | Weights | 3.2 GB | a695e1fb4b0a |
| model-00004-of-00007.safetensors | Weights | 3.2 GB | 1e20bf693675 |
| model-00005-of-00007.safetensors | Weights | 1.8 GB | 2a0ce74eb360 |
| model-00006-of-00007.safetensors | Weights | 2.5 GB | 55a14ee79d3e |
| model-00007-of-00007.safetensors | Weights | 2.5 GB | 6866cf8adccc |
| model_extra_tensors.safetensors | Weights | 849.4 MB | 1d8268aa85ac |
| config.json | Configuration | 4.3 KB | — |
| generation_config.json | Configuration | 214 B | — |
| model.safetensors.index.json | Configuration | 278.4 KB | — |
| preprocessor_config.json | Configuration | 443 B | — |
| processor_config.json | Configuration | 1.2 KB | — |
| quantization_config.json | Configuration | 488 B | — |
| chat_template.jinja | Other | 9.0 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 20.0 MB | 06b9509352d2 |
| tokenizer_config.json | Tokenizer | 1.2 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 20.6 GB
Released by Jl through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 20.6 GB |
| 16-bit | 33.4 GB |
| 8-bit | 16.7 GB |
| 4-bit | 8.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Qwen3.8-27B-AutoRound-NVFP4
How much GPU memory does Qwen3.8-27B-AutoRound-NVFP4 need?
About 40.1 GB at 16-bit and 10 GB at 4-bit: the weights (16.7B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Qwen3.8-27B-AutoRound-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.
What is Qwen3.8-27B-AutoRound-NVFP4's context length?
262,144 tokens, from the maximum position embeddings in its published configuration.