Sold by: Multiverse Computing
Deployed on AWS
Reduce inference costs and increase inference speed by using Multiverse Computing's CompactifAI Llama 3.1 Slim. A 80% compression of the widely known Meta Llama 3.1 8B model.
Overview
Reduce inference costs and increase inference speed by using Multiverse Computing's CompactifAI Llama 3.1 Slim. A 80% compression of the widely known Meta Llama 3.1 8B model.
Highlights
- 50% OpEx reduction
- 2x Faster Inference
Details
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Delivery method
Latest version
Deployed on AWS
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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
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 | Description | Cost/host/hour |
|---|---|---|
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $0.29 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $0.29 |
Dimensions summary
You pay by the hour for model inference on the ml.g5.xlarge instance type. Both options bill on the same instance size, so hardware cost stays consistent. The two options differ by processing mode. Batch mode runs inference on grouped requests, which suits scheduled or bulk workloads. Real-time mode serves live requests for interactive applications. You choose the mode that matches your workload, and billing accrues per host hour of use. There is no upfront commitment; charges scale with the hours you run each mode.
Top-of-mind questions for buyers
You run inference on the ml.g5.xlarge instance type, a single GPU-backed machine. This is a compressed 'Slim' version of the Llama 3.1 8B model, which reduces memory and hardware needs. Billing accrues per host hour that the instance runs, regardless of which mode you choose.
Charges accrue per host hour while the instance runs. When you stop the instance, software host-hour charges stop for that mode. Fully stopped instances do not accrue running-time charges, though underlying AWS storage fees may still apply. You pay only for the hours each mode actually runs.
Batch mode processes grouped requests, which fits scheduled or bulk jobs. Real-time mode serves live requests for interactive applications. Both bill per host hour on the same instance size. You can run each mode separately, and each accrues its own host-hour charges on your invoice.
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Delivery details
Amazon SageMaker model
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:
Version release notes
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Additional details
Inputs
- Summary
Openai request format
- Input MIME type
- application/json
{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What's life?"
}
]
}
{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What's life?"
}
]
}
Support
Vendor support
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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