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Snowflake's Boxin Jiang describes optimizing multimodal AI pipelines

2 articles1 sourcesince Nov 4Last article 1h ago ·

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Snowflake's Boxin Jiang says the company combines model, compiler, and query optimizations to make multimodal AI pipelines reliable and cost-effective, according to a post from LMSYS Org on X. The post links to a full discussion of the approach.

The reports give no figures, benchmarks, or product details, so the claims about reliability and cost are Snowflake's own and remain unverified.

Written by AI from the articles below · updated Nov 4, 5:54 PM ET

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Latest developmentNov 4, 5:51 PM ET
Snowflake's Boxin Jiang on optimizing multimodal AI pipelines at scale

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The articles in this story. Times are ET.

Nov 4
  1. LMSYS OrgOfficial
    Snowflake's Boxin Jiang on optimizing multimodal AI pipelines at scale

    AILMSYS Org shares a post from Snowflake on optimizing multimodal AI pipelines at scale. Boxin Jiang describes combining model, compiler, and query optimizations to make these pipelines reliable and cost-effective.

    Image from @lmsysorg's post
  2. LMSYS OrgOfficial
    Snowflake explains optimizing multimodal AI pipelines at scale

    AISnowflake says Boxin Jiang combines model, compiler, and query optimizations to make multimodal AI pipelines reliable and cost-effective. The post is a short promotional note linking to the full discussion, with no figures or benchmarks given.

    Image from @lmsysorg's post

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