# Qwen3.8-27B-Uncensored by OrcaRouter: Open-Weight Model Source: https://savrn.com/models/qwen3-8-27b-uncensored 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-Uncensored (27.8B 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 | 55.6 GB | 66.7 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 | 27.8 GB | 33.3 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 | 13.9 GB | 16.7 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 | 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 7, 2026. [Qwen3.8-27B-Uncensored on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/qwen3-8-27b-uncensored/gpus) ## Model Card By OrcaRouter, published under apache-2.0, revision 195dfa2804a2. The full-precision BF16 abliterated (refusal-removed) build of Qwen's Qwen3.8-27B — the source for fine-tuning, post-training & quantization | | | --- | | New from Orca — [OrcaCyber Zero 1.0](https://www.orcarouter.ai/models/orca/orcacyber-zero-1.0) Post-trained for cybersecurity: vulnerability analysis, vulnerability reproduction, red teaming & offensive-security research. 98.07% pass@1 on CyberGym Level 1 · 1M context · function calling · OpenAI-compatible API. | One Gateway. Every Model. — Route Smarter · Ship Safer · Spend Less. [Run via API](https://www.orcarouter.ai/models/qwen/qwen3.8-27b) · [API Endpoint](https://api.orcarouter.ai/v1) · [Website](https://www.orcarouter.ai) · [Model Catalog](https://www.orcarouter.ai/models) · [Model Card](https://www.orcarouter.ai/models/qwen/qwen3.8-27b) · [GitHub](https://github.com/Continuum-AI-Corp) · [Discord](https://discord.gg/yAh6Tex6kx) · [X](https://x.com/OrcaRouter) [Read the full model card (1,479 words)](https://savrn.com/models/qwen3-8-27b-uncensored/card) ## Identity and Version Repository orcarouter/Qwen3.8-27B-Uncensored Publisher OrcaRouter Task Image and text to text Modality Image and text Library transformers Parameters 27.8B parameters Languages en, zh Revision 195dfa2804a29cc45f393a5dad8e0bd72e233070 First published 2026-08-18 Last updated 2026-10-02 ## Files and Weights 31 files, 55.6 GB in total. The weights are 18 files totalling 55.6 GB in safetensors. Weights18 files · 55.6 GB Configuration5 files · 117.5 KB Tokenizer4 files · 22.9 MB Documentation2 files · 27.2 KB Other1 file · 9.0 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00018.safetensors | Weights | 4.0 GB | — | | model-00002-of-00018.safetensors | Weights | 3.0 GB | — | | model-00003-of-00018.safetensors | Weights | 2.5 GB | — | | model-00004-of-00018.safetensors | Weights | 4.0 GB | — | | model-00005-of-00018.safetensors | Weights | 2.1 GB | — | | model-00006-of-00018.safetensors | Weights | 4.0 GB | — | | model-00007-of-00018.safetensors | Weights | 2.1 GB | — | | model-00008-of-00018.safetensors | Weights | 4.0 GB | — | | model-00009-of-00018.safetensors | Weights | 2.1 GB | — | | model-00010-of-00018.safetensors | Weights | 4.0 GB | — | | model-00011-of-00018.safetensors | Weights | 2.1 GB | — | | model-00012-of-00018.safetensors | Weights | 4.0 GB | — | | model-00013-of-00018.safetensors | Weights | 2.1 GB | — | | model-00014-of-00018.safetensors | Weights | 4.0 GB | — | | model-00015-of-00018.safetensors | Weights | 2.1 GB | — | | model-00016-of-00018.safetensors | Weights | 4.0 GB | — | | model-00017-of-00018.safetensors | Weights | 2.1 GB | — | | model-00018-of-00018.safetensors | Weights | 3.4 GB | — | | config.json | Configuration | 4.3 KB | — | | generation_config.json | Configuration | 202 B | — | | model.safetensors.index.json | Configuration | 112.2 KB | — | | preprocessor_config.json | Configuration | 390 B | — | | video_preprocessor_config.json | Configuration | 385 B | — | | LICENSE | Documentation | 11.5 KB | — | | README.md | Documentation | 15.6 KB | — | | chat_template.jinja | Other | 9.0 KB | — | | .gitattributes | Repository | 1.6 KB | — | | merges.txt | Tokenizer | 3.4 MB | — | | tokenizer.json | Tokenizer | 12.8 MB | — | | tokenizer_config.json | Tokenizer | 17.9 KB | — | | vocab.json | Tokenizer | 6.7 MB | — | ## License and Download License apache-2.0 Access Access requested at publisher Download size 55.6 GB [Request access from OrcaRouter](https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored) OrcaRouter grants access 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) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 55.6 GB | | 16-bit | 55.6 GB | | 8-bit | 27.8 GB | | 4-bit | 13.9 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Merged from[XORTRON-RICO-v3](https://savrn.com/models/xortron-rico-v3) - Derived from[XORTRON-RICO-v3](https://savrn.com/models/xortron-rico-v3) - Quantized from[Qwen3.8-27B-Uncensored-NVFP4-v100-skinny](https://savrn.com/models/qwen3-8-27b-uncensored-nvfp4-v100-skinny) - Derived from[Qwen3.8-27B-Uncensored-NVFP4-v100-skinny](https://savrn.com/models/qwen3-8-27b-uncensored-nvfp4-v100-skinny) ## Questions About Qwen3.8-27B-Uncensored ### How much GPU memory does Qwen3.8-27B-Uncensored 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 Qwen3.8-27B-Uncensored 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-Uncensored commercially? Yes. Qwen3.8-27B-Uncensored 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. ## Similar Models Model · Image and text to text ### [Qwen3.8-27B](https://savrn.com/models/qwen3-8-27b) [Qwen](https://savrn.com/model-publishers/qwen) 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. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. 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Qwen3.8 can be… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-8-27b-fp8) Model · Image and text to text ### [Qwen3.6-27B](https://savrn.com/models/qwen3-6-27b) [Qwen](https://savrn.com/model-publishers/qwen) Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. This release delivers substantial upgrades, particularly in For more details, please refer to our blog post Qwen3.6-27B. Empty cells (--) indicate scores not yet available or not applicable. For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. Qwen3.6 can be served via APIs with popular inference frameworks. 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Empty cells (--) indicate scores not… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-27b) Model · Image and text to text ### [JEV-27B-VL](https://savrn.com/models/jev-27b-vl) [AutoTrust AI Lab](https://savrn.com/model-publishers/autotrust) autotrust/JEV-27B-VL is autotrust/JEV-27B with vision. Every step below is one System 1 decision: a camera image or a screenshot in, a probability for every action out, in a single forward pass. Robot arm: pick and place from a camera image. At every step System 1 looks at the top camera image and answers two questions: is the target left or right of the gripper, and above or below it? The arm moves accordingly and halves its step whenever an answer flips. It grasps the cube, carries it and drops it in the tray (MuJoCo simulation). 75% of 20 random scenes completed, the best of the JEV family. Every cube it grasped ended in the tray (15 of 15); every miss was a grasp 3.0–3.7 cm off target.… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/jev-27b-vl) Model · Image and text to text ### [Qwen3.8-27B-AWQ-INT4](https://savrn.com/models/qwen3-8-27b-awq-int4) [Cyankiwi](https://savrn.com/model-publishers/cyankiwi) 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. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. Qwen3.8 can be… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-8-27b-awq-int4) ## OrcaRouter [All models and datasets](https://savrn.com/model-publishers/orcarouter) ## Versions - [195dfa2804a2](https://savrn.com/models/qwen3-8-27b-uncensored/versions/195dfa2804a2) · current 2026-10-02 ## Explore More - [All image and text to text models](https://savrn.com/models/tasks/image-and-text-to-text) - [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 2026-10-02. - [Hugging Face record](https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored) - [How the hub is built](https://savrn.com/model-hub/methodology)