Sold by: Salient Engineering
Deployed on AWS
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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.
Overview
This is a self-hosted deployment of the Qwen 3.8 27B 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, allow up to one hour for the LLM to finish loading before sending requests. Highlights of the Qwen 3.8 model include:
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Dense 27B causal language model with a native vision encoder for image and video understanding, including STEM diagrams, documents, and hour-scale video.
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Native context window of 262,144 tokens, extensible up to 1,000,000 tokens with YaRN.
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Thinking mode is on by default and can be disabled per request; reasoning depth is tunable with reasoning_effort, and preserve_thinking retains reasoning context across turns.
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Stronger agent execution for autonomous planning and end-to-end task completion, with published scores including 61.7 on SWE-bench Pro and 84.3 on OSWorld-Verified.
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Hybrid Gated DeltaNet and Gated Attention architecture, with Multi-Token Prediction (MTP).
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Fully open-source under the Apache 2.0 license.
Highlights
- Data security, privacy, and confidentiality
- Predictable cost
- Unlimited usage of a dedicated model
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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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 |
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 model is now preloaded into GPU memory on boot so the first prompt does not wait for a cold load. Please allow up to one hour after the instance is powered on for the Ollama service to finish loading the LLM before sending requests. Ollama is exposed on port 11434.
Test via HTTP with: curl -X POST http://<PUBLIC_IP>:11434/api/generate -d '{"model":"qwen3.8:27b","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 one hour for the server to preload the LLM into GPU memory before sending requests. The model is loaded on boot; you do not need to wait for the first prompt to trigger the load.
- Access the Qwen 3.8 27B 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":"qwen3.8:27b","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.
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