Sold by: Salient Engineering
Deployed on AWS
Free Trial
A self-hosted, production-ready Qwen 3.8 27B Uncensored model deployed into your AWS environment with a single click. Because everything runs entirely within your private cloud, your data stays secure, isolated, and fully under your control. Best of all, unlimited tokens.
Overview
This is a self-hosted deployment of the Qwen 3.8 27B Uncensored large language model. It runs as a single GPU-powered EC2 instance allowing you to keep your data private and leverage unlimited tokens. Access to the model is via HTTPS, ensuring data is encrypted in-transit at all times. The model is preloaded into GPU memory on boot, so the first prompt does not wait for a cold load. Once the instance is powered on, customers should allow the server appliance up to 90 minutes to boot before the Ollama service will be reachable. Highlights of the Qwen 3.8 Uncensored model include:
-
Abliterated (refusal-removed) dense 27B hybrid-attention vision-language model, with the vision tower and MTP speculative-decoding head preserved from Qwen 3.8 27B.
-
Native context window of 262,144 tokens.
-
Thinking mode is on by default and can be toggled per request; tool calling is supported.
-
Native vision encoder for image understanding, including on-image text (OCR).
-
Hybrid Gated DeltaNet and full-attention architecture (64 layers).
-
Capability on MMLU, MMLU-Pro, GSM8K, and CMMLU remains within 1.3 points of the official Qwen 3.8 27B FP8 base on the published card.
-
Released under the Apache 2.0 license, inherited from the base model. Intended for research, interpretability, red-teaming, and controlled evaluation; add your own safety and moderation layers before any end-user deployment.
Highlights
- Data security, privacy, and confidentiality
- Predictable cost
- Unlimited usage of a dedicated model
Details
Sold by
Categories
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free for 5 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
g6e.2xlarge Recommended | $0.09 |
Dimensions summary
You pay a single hourly software fee tied to one GPU instance size, the g6e.2xlarge. Billing is usage-based and measured in hours. There are no per-token charges, so the model runs with unlimited use on that instance while it is running. The model is self-hosted in your own AWS account, so your AWS infrastructure costs are separate from this software fee. A free trial is available on AWS Marketplace.
Top-of-mind questions for buyers
The rate covers the model software running on one g6e.2xlarge GPU instance in your AWS account. You run the model there with no per-token charge. The underlying AWS compute, storage, and network costs are separate and billed by AWS directly.
The hourly software fee meters running time on the g6e.2xlarge instance. When the instance is stopped, the software fee stops too. Stopped instances may still incur AWS storage charges for attached volumes, but those are AWS costs, not this software fee.
No. The fee is tied to instance-hours, not to usage volume. You get unlimited use of the model on that instance while it runs. The 262K context window, thinking mode, tool calling, and image understanding are available at the same hourly rate.
www.salientengineering.com
Helpful?
Vendor refund policy
Refunds may be considered on a per-case basis. Please contact us at support@salientengineering.com for inquiries.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
The Ollama service configuration has been updated to export OLLAMA_ORIGINS="*" for cross-origin browser and web application support. The model is preloaded into GPU memory on boot so prompts do not wait for a cold load. Customers should allow the server appliance up to 90 minutes to boot before the Ollama service will be reachable. Ollama is exposed on port 11434.
Test via HTTP with: curl -X POST http://<PUBLIC_IP>:11434/api/generate -d '{"model":"orcarouter/Qwen3.8-27B-Uncensored","prompt":"In one sentence, explain what a large language model is capable of."}'
Additional details
Usage instructions
- Deploy the EC2 instance, configure the Security Group to only allow inbound port 22 and 11434 from your trusted IP address(es)
- After the instance is powered on, allow up to 90 minutes for the server appliance to boot and preload the LLM into GPU memory before sending requests.
- Access the Qwen 3.8 27B Uncensored model via the Ollama service exposed on port 11434 for HTTP.
Test via HTTP with: curl -X POST http://<PUBLIC_IP>:11434/api/generate -d '{"model":"orcarouter/Qwen3.8-27B-Uncensored","prompt":"In one sentence, explain what a large language model is capable of."}'
Resources
Vendor resources
Support
Vendor support
The Salient Engineering support team can be reached at: support@salientengineering.com
Our team is happy to assist with deployment and configuration issues.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
A self-hosted, production-ready Qwen 3.8 27B model deployed into your AWS environment with a single click. Because everything runs entirely within your private cloud, your data stays secure, isolated, and fully under your control. Best of all, unlimited tokens.
A self-hosted, production-ready Qwen 3.6 35B model, with 3 billion active parameters deployed into your AWS environment with a single click. Because everything runs entirely within your private cloud, your data stays secure, isolated, and fully under your control. Best of all, unlimited tokens.
Chat, reasoning and image understanding from the open Qwen3.8-27B model, served on one GPU with vLLM. Runs on a SageMaker endpoint in your own AWS account with no network access, so prompts and images are not sent to a third-party API.
A self-hosted, production-ready Qwen 2.5 7B model deployed into your AWS environment with a single click. Because everything runs entirely within your private cloud, your data stays secure, isolated, and fully under your control. Best of all, unlimited tokens.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.