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.
This is a self-hosted deployment of the Qwen 3.6 35B-A3B 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. Highlights of the Qwen 3.6 35B model include:
Utilizes a sparse Mixture-of-Experts (MoE) architecture with 35 billion total parameters and only 3 billion active parameters to deliver flagship-level performance with massive efficiency.
Features exceptional agentic coding capabilities that surpass much larger dense models and excel at repository-level reasoning and terminal-based workflows.
Introduces the "preserve thinking" feature, which retains reasoning traces from all preceding conversation turns to improve consistency in complex, multi-step tasks.
Native multimodal support enables high-performance perception and reasoning across text, images, and video, particularly in tasks requiring spatial intelligence.
Supports a standard context window of 262K tokens, which can be extended up to 1 million tokens using specialized techniques like YaRN.
Incorporates Multi-Token Prediction (MTP) to enable speculative decoding, significantly increasing inference speed for structured data and code generation.
Provides flexible interaction through a native "Thinking Mode" for deep reasoning and a "Non-Thinking Mode" for instant, direct responses.
Fully open-source under the Apache 2.0 license, allowing for unrestricted commercial use and local deployment on consumer-grade hardware.
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.
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.
You pay for this model by the hour on a single deployment size: the g5.2xlarge GPU instance. Billing is usage-based, so charges accrue only while the instance runs. There are no tiers or plans to choose between. Your cost scales with how many hours you keep the instance active. This setup runs a vision-capable language model for coding, reasoning, and multimodal tasks. To control spend, stop the instance when it is not in use.
Top-of-mind questions for buyers
What compute resources do I get with the g5.2xlarge hourly rate?
The g5.2xlarge is a single GPU instance sized to run this model. You pay per hour it runs. The rate covers the software license on that one instance type. Underlying AWS infrastructure charges for the instance are separate and appear on your AWS bill.
Am I charged when the instance is stopped or idle?
Hourly software charges accrue only while the g5.2xlarge instance runs. A fully stopped instance stops accruing software charges. Stopped instances may still incur AWS storage fees for attached volumes, but the software license meters running time only. Stop the instance to control spend.
Does the hourly rate change based on context length or number of tokens processed?
No. Billing is by instance-hour, not by tokens or requests. You pay the same hourly rate regardless of how many tokens you process or how long your context window is. The model supports context lengths up to 262,144 tokens natively without changing your hourly cost.
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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
In this release, we've fixed some bugs and optimized the GPU for maximum token throughput. Configured for production environments, please allow 10 minutes once launched for the Ollama service to fully boot the model. Ollama is exposed on ports 443 and 11434.
Test via HTTPS with: curl -X POST -k https:///api/generate -d '{"model":"qwen3.6:35b-a3b","prompt":"In one sentence, explain what a large language model is capable of."}'
Test via HTTP with: curl -X POST http://:11434/api/generate -d '{"model":"qwen3.6:35b-a3b","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, 443, and 11434 from your trusted IP address(es)
Access the Qwen 3.6 35B-A3B model via the Ollama service exposed on port 443 for HTTPS or port 11434 for HTTP.
Test via HTTPS with: curl -X POST -k https://<Instance Public DNS Address>/api/generate -d '{"model":"qwen3.6:35b-a3b","prompt":"In one sentence, explain what a large language model is capable of."}'
Test via HTTP with: curl -X POST http://<Instance Public DNS Address>:11434/api/generate -d '{"model":"qwen3.6:35b-a3b","prompt":"In one sentence, explain what a large language model is capable of."}'
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This product has charges associated with it for seller support. Run & Manage latest LLMs locally, privately, securely and cost-effectively without any vendor lock-in.
This VM solution comes pre-loaded with LLaMA, Mistral, Gemma, DeepSeek, & Qwen models along with Open-WebUI as an intuitive UI to interact with the LLMs and Ollama to install new models as needed.
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