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Available as a NIM, Llama 3.1 70B-Instruct is a 70B-parameter LLM pretrained, instruction tuned model optimized for multilingual dialogue.
Overview
Meta Llama 3.1 is a collection of multilingual large language models (LLMs) that are pre-trained and instruction-tuned generative models. NVIDIA NIM microservices for Llama 3.1 70B-Instruct simplifies the deployment of the Llama 3.1 70B instruction tuned model which is optimized for language understanding, reasoning, and text generation use cases. Llama 3.1 70B-Instruct is available as an NVIDIA NIM microservice, part of NVIDIA AI Enterprise available on the AWS Marketplace. NIM is a set of easy-to-use microservices designed for secure, reliable deployment of high performance AI model inferencing across clouds, data centers and workstations.
The Llama 3.1 70B-Instruct NIM is a prebuilt container that includes the Meta Llama 3.1 large language model built on inference engines like Triton Inference Server, TensorRT, TensorRT-LLM, and PyTorch. NIM provides features like low latency, high throughput, function calling, metrics export, standard API, optimized profiles & enterprise support.
Highlights
- NVIDIA Llama 3.1 70B-Instruct is an 70-billion-parameter multilingual large language model (LLM) pretrained and instruction tuned generative model. The Llama 3.1 instruction tuned text only model is optimized for multilingual dialogue use cases. It is available as an [NVIDIA NIM microservice](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html).
- NVIDIA NIM, a part of the [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/) software platform available on the [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-ozgjkov6vq3l6), is a set of easy-to-use microservices designed for secure, reliable deployment of high performance AI model inferencing.
Details
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Pricing
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Try this product free for 90 days according to the free trial terms set by the vendor.
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.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $8.00 |
ml.p5.48xlarge Inference (Real-Time) Recommended | Model inference on the ml.p5.48xlarge instance type, real-time mode | $8.00 |
Dimensions summary
You pay by the host hour, based on the AWS instance type you run and the inference mode you choose. The ml.g5.48xlarge option runs batch inference, which processes requests in groups. The ml.p5.48xlarge option runs real-time inference, which responds to requests as they arrive. These two options are independent, not stacked tiers. Your cost scales with the hours each instance stays active. Pick the instance and mode that fit your workload; both bill on the same per-host-hour basis.
Top-of-mind questions for buyers
Both bill per host hour of active instance time. Batch mode on ml.g5.48xlarge processes requests in groups, which suits scheduled or high-volume jobs. Real-time mode on ml.p5.48xlarge answers requests as they arrive, which suits interactive workloads. You pick one based on how your traffic flows.
Charges accrue per host hour while the instance runs. When you stop the instance, software host-hour charges stop with it. You control cost by shutting down instances you are not using. Note that underlying AWS storage or reserved resources may still incur separate AWS fees.
Each host hour runs the Llama 3.1 70B-Instruct model as a NIM container on one AWS GPU instance. The container packages curated model weights and a validated runtime, so you deploy without tuning the setup yourself. One host hour equals one hour of that instance running the microservice.
docs.nvidia.com
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No refunds. Please contact NVIDIA at https://www.nvidia.com/en-us/data-center/lp/aws-marketplace-offer/ for further assistance.
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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
Supports Real-time inference on NVIDIA H100: P5.48xlarge instance
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters on /invocations and /ping APIs that can be used to control the generated text. See examples and fields descriptions below.
- Input MIME type
- application/json
{
"model": "meta/llama-3.1-8b-instruct",
"messages": "[
{"role": "user", "content": "Hello! How are you?"},
{"role": "assistant", "content": "Hi! I am quite well, how can I help you today?"},
{"role": "user", "content": "What is the capital of France? Give one word answer."}
]",
"max_tokens": 4,
"stream":false
}
https://github.com/NVIDIA/nim-deploy/tree/main/cloud-service-providers/aws/sagemaker
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
model | Name of the model: meta/llama-3.1-8b-instruct
| Type: FreeText | Yes |
messages | Text input for the model to respond to. | Type: FreeText | Yes |
max_tokens | The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations. | Default value: 1024
Type: FreeText | No |
stream | When `true`, the response will be a JSON stream of events. If set to `false`, the entire response will be sent out to client.
| Default value: false
Type: Categorical
Allowed values: true, false | No |
temperature | Use a lower value to decrease randomness in the response. Randomness can be further maximized by increasing the value of the `p` parameter.
| Default value: 0.5
Type: Continuous
Minimum: 0
Maximum: 2 | No |
Resources
Support
Vendor support
Free support via NVIDIA NIM Developer Forum: https://forums.developer.nvidia.com/c/ai-data-science/nvidia-nim/
Global enterprise support is included with an NVIDIA AI Enterprise subscription: https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/
For additional support information please contact NVIDIA: https://www.nvidia.com/en-us/data-center/lp/aws-marketplace-offer
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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