Sold by: HolboxAI
Deployed on AWS
Private OpenAI-compatible vLLM endpoint serving Qwen3-4B on a GPU EC2 instance in your account. Function/tool calling and 32K context. Returns an endpoint URL and API key.
Overview
Qwen 3.5 4B Inference Endpoint deploys a private, OpenAI-compatible large language model endpoint into your own AWS account. It serves the Qwen3.5 4B model with vLLM on a single GPU EC2 instance, so your prompts and data never leave your environment.
The endpoint is OpenAI API compatible (v1/chat/completions, v1/models) and supports function/tool calling and a 32K token context window, making it a drop-in local model for IDE coding agents such as Cursor, Cline, and Continue, as well as your own apps.
The AMI is self-contained: on first boot it starts vLLM, generates an API key, and serves on port 8000. First boot downloads the model (about 18 to 25 minutes). You pay only for the EC2 GPU instance while it runs; terminate the instance to stop all charges.
Highlights
- Private OpenAI-compatible Qwen3.5 4B endpoint that runs entirely in your own AWS account - prompts and data never leave your environment.
- Function/tool calling and 32K token context out of the box - works as a local model for IDE agents like Cursor, Cline, and Continue.
- Self-contained AMI: boots vLLM automatically, generates an API key, and serves on port 8000. Pay only for the EC2 GPU while it runs.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
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Pricing
This product is available free of charge. Free 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.
Dimensions summary
This listing deploys the Qwen-3.5 4B inference endpoint, and the software itself is free. You pay only for the AWS GPU instance you run it on, billed by the hour. The six dimensions map to different EC2 GPU instance types across two families, each in an xlarge and 2xlarge size. The 2xlarge sizes give more compute per hour than their matching xlarge counterparts. Choose the instance type that fits your throughput and memory needs. Because billing is hourly, your cost scales with how long each instance stays running.
Top-of-mind questions for buyers
Each dimension maps to an EC2 GPU instance. The model targets a GPU with 24GB of memory. The xlarge sizes give a smaller core count, while the 2xlarge sizes give more vCPUs and system memory per hour. Pick the size that fits your throughput and memory needs.
The software itself is free, so no software charge applies. Hourly billing on this listing meters running instance time only. A stopped instance stops accruing hourly compute charges. Underlying AWS storage for a stopped instance may still bill separately through AWS.
The 2xlarge sizes carry more vCPUs and memory per hour than their matching xlarge counterparts. That extra compute can support more concurrent requests. Your realized throughput still depends on request size and model load. Load testing before production can measure time-to-first-token and throughput on your deployment.
www.holbox.ai
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Vendor refund policy
This product is offered free of charge by Holbox AI. There are no software charges, and therefore nothing to refund through this listing.
You remain responsible for the AWS infrastructure costs of running it - the EC2 instance and its EBS root volume. Those are billed by AWS, not by Holbox AI, and are not refundable through this listing. Stop or terminate instances when they are idle. For questions about AWS charges, contact AWS Support or see https://aws.amazon.com/marketplace/help/
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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
Initial release. Qwen-3.5 4B on vLLM. Hermes tool/function calling, 32K context, OpenAI-compatible API on port 8000
Additional details
Usage instructions
-
Launch the product on a recommended GPU instance (g5.xlarge or larger) in a public subnet. In the security group, allow inbound TCP 8000 from your IP or CIDR.
-
First boot downloads the model and starts vLLM (about 18 to 25 minutes). The service starts automatically - no SSH or extra configuration is required.
-
Retrieve the endpoint URL and API key without SSH:
- Serial console: EC2 console > select the instance > Actions > Monitor and troubleshoot > Get system log. Look for the "Qwen4B endpoint" banner with the Endpoint and API key.
- Optional (SSM Parameter Store): if you attach an IAM instance profile that grants ssm:PutParameter on /qwen4b/*, the boot script also publishes the endpoint and key to /qwen4b/INSTANCE_ID/endpoint and /qwen4b/INSTANCE_ID/api_key.
-
Call the endpoint (model name is qwen4b): curl http://PUBLIC_IP:8000/v1/chat/completions
-H "Authorization: Bearer YOUR_API_KEY"
-H "Content-Type: application/json"
-d '{"model":"qwen4b","messages":[{"role":"user","content":"Hello"}]}' -
Tool/function calling: pass an OpenAI-style "tools" array; the endpoint returns structured tool_calls (Hermes parser). Context window is 32768 tokens.
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To stop all charges, terminate the instance (or delete the CloudFormation stack if you launched via a template).
Support
Vendor support
Support email: support@holbox.ai
Support description: Support is provided via email for product installation, deployment, configuration, and troubleshooting. Assistance includes AMI startup problems, endpoint availability, and product-specific questions. Please include your AWS Region, CloudFormation stack events, and relevant logs when contacting support to help us investigate issues more efficiently.
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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