# Qwen3.8-27B-NVFP4 by Unsloth AI: Open-Weight Model Source: https://savrn.com/models/qwen3-8-27b-nvfp4 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 19.9 GB | 23.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 9.9 GB | 11.9 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026. [Qwen3.8-27B-NVFP4 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/qwen3-8-27b-nvfp4/gpus) ## 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. SAVRN Research ## 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_head is FP8 rather than 4-bit. ### Read our How to [Run Qwen3.8-27B Guide!](https://unsloth.ai/docs/models/qwen3.8) 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](https://unsloth.ai/docs/new/desktop). [Read our guide](https://unsloth.ai/docs/models/qwen3.8). - 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. [Read the full model card (731 words)](https://savrn.com/models/qwen3-8-27b-nvfp4/card) ## 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. Weights2 files · 23.4 GB Configuration5 files · 187.9 KB Tokenizer3 files · 26.7 MB Documentation1 file · 6.7 KB Other1 file · 10.0 KB Repository1 file · 1.6 KB 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 [Download from Unsloth AI](https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4) Released by Unsloth AI through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [Qwen/Qwen3.8-27B](https://savrn.com/models/qwen3-8-27b) - Quantized from [Qwen/Qwen3.8-27B](https://savrn.com/models/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. ## Unsloth AI [All models and datasets](https://savrn.com/model-publishers/unsloth) ## Versions - [f0b7c9e722f5](https://savrn.com/models/qwen3-8-27b-nvfp4/versions/f0b7c9e722f5) · current 2026-10-23 ## Explore More - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4) - [How the hub is built](https://savrn.com/model-hub/methodology)