In the fast-paced world of AI development, the regional availability of large language models (LLMs) can make a significant difference in the competitive advantage of enterprises. Companies that have early access to LLMs can innovate faster and stay ahead of the curve. However, many organizations face challenges when it comes to accessing models due to resource constraints, western-centric bias, and multilingual barriers. This can lead to delays in leveraging the latest AI technology and falling behind competitors.
To address the critical obstacle of regional availability, Snowflake has introduced cross-region inference as a solution. This feature allows developers to process requests on Cortex AI in a different region, even if the desired model is not yet available in their source region. By enabling cross-region inference, organizations can seamlessly integrate with the LLM of their choice, regardless of its regional availability. This enables the integration of new LLMs as soon as they become available.
In order to utilize cross-region inference on Cortex AI, developers must first enable the feature and specify the regions for inference processing. Data traversal between regions is facilitated through secure channels, with encryption mechanisms in place to protect the data in transit. If both regions are on Amazon Web Services (AWS), data will remain within the AWS global network and be encrypted at the physical layer. For regions on different cloud providers, traffic will be securely transmitted via encrypted transport layer security (MTLS) over the public internet.
One of the key benefits of cross-region inference is the ability to execute inference and generate responses within the secure Snowflake perimeter. By setting account-level parameters, users can configure where inference processing will take place. Cortex AI will automatically select a region for processing in case the requested LLM is not available in the source region. This flexibility allows organizations to leverage LLMs across different regions without incurring additional egress charges.
As an example, consider a scenario where Snowflake Arctic is used to summarize a paragraph. If the model is not available in the source region (AWS U.S. east), Cortex AI will route the request to an alternative region where the model is available (e.g., AWS U.S. west 2). The response will then be sent back to the source region seamlessly. The entire process can be achieved with a single line of code, making it both efficient and cost-effective for users.
Cross-region inference provides organizations with the flexibility to leverage large language models across different regions without being constrained by regional availability issues. By enabling developers to seamlessly integrate with LLMs of their choice, regardless of regional constraints, Snowflake’s cross-region inference feature opens up new possibilities for innovation and collaboration in the field of AI development.
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