Reduce inference costs and increase inference speed by using Multiverse Computing's CompactifAI HyperNova 60B. In direct benchmarks against Mistral 3 Large, HyperNova 60B delivers outstanding gains:
Reduce inference costs and increase inference speed by using Multiverse Computing's CompactifAI HyperNova 60B. In direct benchmarks against Mistral 3 Large, HyperNova 60B delivers outstanding gains.
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.
You pay by the host hour based on the AWS instance type you run and your inference mode. Pricing has two modes: batch processing for grouped workloads and real-time for live responses. Batch mode runs on two instance types, each priced separately. Real-time mode runs on four instance types, each priced separately. Larger or more powerful instances carry higher hourly rates. You choose the instance that fits your performance needs and pay only for the hours you use, with no upfront commitment.
Top-of-mind questions for buyers
What does one host hour cover for billing?
One host hour is one hour that a single inference instance runs the model. You are billed per running instance per hour. Charges accrue while the instance is active, whether in batch or real-time mode. The rate depends on which AWS instance type you select.
How does batch mode billing differ from real-time mode?
Batch mode processes grouped workloads and runs on two instance types. Real-time mode serves live responses and runs on four instance types. Both bill per host hour while the instance runs. Batch suits scheduled or bulk jobs. Real-time suits interactive, on-demand requests needing immediate output.
Am I charged when an inference instance is stopped or idle?
Software charges apply per host hour while the instance runs. A fully stopped instance stops accruing host hour charges. Underlying AWS infrastructure fees, such as storage, may still apply separately. The software meter counts running time only, in both batch and real-time modes.
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An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Real-time inference
Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .
Batch transform
Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .
Version release notes
CompactifAI HyperNova 60B inference on a vLLM engine
Additional details
Inputs
Outputs
Usage instructions
Sample notebooks
Inputs
Summary
Our model accepts an input compatible with openAI's chat completions endpoint.
Input MIME type
application/json
Real-time inference sample input data
{ "model": "cai-hypernova-60b", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me about quantum computing."} ] }
Batch transform sample input data
{ "model": "cai-hypernova-60b", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me about quantum computing."} ] }
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