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Mercury foundation models from Inception Labs are now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart | Artificial Intelligence

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Mercury foundation models from Inception Labs are now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Today, we are excited to announce that Mercury and Mercury Coder foundation models (FMs) from Inception Labs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart . With this launch, you can deploy the Mercury FMs to build, experiment, and responsibly scale your generative AI applications on AWS.

In this post, we demonstrate how to get started with Mercury models on Amazon Bedrock Marketplace and SageMaker JumpStart.

About Mercury foundation models

Mercury is the first family of commercial-scale diffusion-based language models, offering groundbreaking advancements in generation speed while maintaining high-quality outputs. Unlike traditional autoregressive models that generate text one token at a time, Mercury models use diffusion to generate multiple tokens in parallel through a coarse-to-fine approach, resulting in dramatically faster inference speeds. Mercury Coder models deliver the following key features:

Ultra-fast generation speeds of up to 1,100 tokens per second on NVIDIA H100 GPUs, up to 10 times faster than comparable models

High-quality code generation across multiple programming languages, including Python, Java, JavaScript, C++, PHP, Bash, and TypeScript

Strong performance on fill-in-the-middle tasks, making them ideal for code completion and editing workflows

Transformer-based architecture, providing compatibility with existing optimization techniques and infrastructure

Context length support of up to 128,000 tokens out of the box

About Amazon Bedrock Marketplace

Amazon Bedrock Marketplace plays a pivotal role in democratizing access to advanced AI capabilities through several key advantages:

Comprehensive model selection – Amazon Bedrock Marketplace offers an exceptional range of models, from proprietary to publicly available options, so organizations can find the perfect fit for their specific use cases.

Unified and secure experience – By providing a single access point for models through the Amazon Bedrock APIs, Amazon Bedrock Marketplace significantly simplifies the integration process. Organizations can use these models securely, and for models that are compatible with the Amazon Bedrock Converse API, you can use the robust toolkit of Amazon Bedrock, including Amazon Bedrock Agents , Amazon Bedrock Knowledge Bases , Amazon Bedrock Guardrails , and Amazon Bedrock Flows .

Scalable infrastructure – Amazon Bedrock Marketplace offers configurable scalability through managed endpoints, so organizations can select their desired number of instances, choose appropriate instance types, define custom automatic scaling policies that dynamically adjust to workload demands, and optimize costs while maintaining performance.

Deploy Mercury and Mercury Coder models in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models through Amazon Bedrock. To access the Mercury models in Amazon Bedrock, complete the following steps:

On the Amazon Bedrock console, in the navigation pane under Foundation models , choose Model catalog .

You can also use the Converse API to invoke the model with Amazon Bedrock tooling.

On the Model catalog page, filter for Inception as a provider and choose the Mercury model.

The Model detail page provides essential information about the model’s capabilities, pricing structure, and implementation guidelines. You can find detailed usage instructions, including sample API calls and code snippets for integration.

To begin using the Mercury model, choose Subscribe .

On the model detail page, choose Deploy .

You will be prompted to configure the deployment details for the model. The model ID will be prepopulated.

For Endpoint name , enter an endpoint name (between 1–50 alphanumeric characters).

For Number of instances , enter a number of instances (between 1–100).

For Instance type , choose your instance type. For optimal performance with Nemotron Super, a GPU-based instance type like ml.p5.48xlarge is recommended.

Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might want to review these settings to align with your organization’s security and compliance requirements.

Choose Deploy to begin using the model.

When the deployment is complete, you can test its capabilities directly in the Amazon Bedrock playground.This is an excellent way to explore the model’s reasoning and text generation abilities before integrating it into your applications. The playground provides immediate feedback, helping you understand how the model responds to various inputs and letting you fine-tune your prompts for optimal results. You can use these models with the Amazon Bedrock Converse API.

SageMaker JumpStart overview

SageMaker JumpStart is a fully managed service that offers state-of-the-art FMs for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval. It provides a collection of pre-trained models that you can deploy quickly, accelerating the development and deployment of ML applications. One of the key components of SageMaker JumpStart is model hubs, which offer a vast catalog of pre-trained models, such as Mistral, for a variety of tasks.

You can now discover and deploy Mercury and Mercury Coder in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK, and derive model performance and MLOps controls with Amazon SageMaker AI features such as Amazon SageMaker Pipelines , Amazon SageMaker Debugger , or container logs. The model is deployed in a secure AWS environment and in your VPC, helping support data security for enterprise security needs.

Prerequisites

To deploy the Mercury models, make sure you have access to the recommended instance types based on the model size. To verify you have the necessary resources, complete the following steps:

On the Service Quotas console, under AWS...