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What is Lamini?

Lamini is an AI tool that offers full-stack production LLM pods for scaling and applying LLM compute within a startup program. Trusted by AI-first companies and partnered with leading data companies, Lamini's production LLM pods incorporate best practices in AI and HPC for efficient building, deploying, and improving LLM models. With complete control over data privacy and security, users can deploy custom models privately on-premise or in VPC, with easy portability across different environments. The platform provides self-serve and enterprise-class support, empowering engineering teams to train LLMs for various use cases efficiently. Seamless compute integration with AMD gives users significant advantages in performance, cost-effectiveness, and availability. Lamini also offers simple pricing tiers and advanced features for big models and enterprise clients. Their Lamini Auditor ensures observability, explainability, and auditing for developers specializing in LLM use cases, aiming to make building customizable superintelligence accessible to all.

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⭐ Lamini Core features

  • ✔️ Full-stack production LLM pods
  • ✔️ Incorporation of best practices in AI and HPC
  • ✔️ Complete control over data privacy and security
  • ✔️ Seamless compute integration with AMD
  • ✔️ Lamini Auditor ensures observability, explainability, and auditing

⚙️ Lamini use case ideas

  1. Utilize Lamini's production LLM pods to scale and apply LLM compute within a startup program, incorporating best practices in AI and HPC for efficient model building and deployment, empowering startups to leverage advanced AI technology without extensive resources or expertise.
  2. Deploy custom LLM models privately on-premise or in VPC while maintaining full control over data privacy and security, ensuring compliance with regulatory requirements and industry standards, suitable for organizations handling sensitive data.
  3. Benefit from Lamini's seamless compute integration with AMD, offering significant performance advantages, cost-effectiveness, and availability, particularly advantageous for engineering teams working on big models and enterprise-level projects.

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