LLM inference & tuning
for Enterprise.
Super fast. Extremely secure.

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Improve accuracy, dramatically reduce hallucinations

  • Guaranteed structured output with optimized JSON decoding
  • Photographic-memory through retrieval-augmented finetuning
  • DPO training with human preferences
  • Integrated RAG-Finetuning framework
  • Evaluation frameworks for tuned models
Time to Market

Accelerate development, with no rate limits

  • Highly parallelized inference for large batch inference
  • Parameter-efficient finetuning that scales to millions of production adapters
  • Infrastructure-agnostic: hybrid cloud VPCs, on-premise airgapped clusters, Nvidia or AMD GPUs.
  • Scalable costs with ROI, e.g. inference on 1 million docs: $80 on Lamini vs. $50.000 on Claude 3

Our Leadership

Sharon Zhou

Co-Founder & CEO
  • Stanford CS Faculty in Generative AI
  • Stanford CS PhD in Generative AI (Andrew Ng)
  • MIT Technology Review 35 Under 35, for award-winning research in generative AI
  • Created largest Coursera courses (Generative AI)
  • Google Product Manager
  • Harvard Classics & CS

Greg Diamos

Co-Founder & CTO
  • MLPerf Co-founder, industry standard for ML perf
  • Landing AI Engineering Head
  • Baidu Head of SVAIL, deployed LLM to 1+ billion users; led 125+ engineers
  • 14,000 citations: AI scaling laws, Tensor Cores
  • NVIDIA, CUDA architect - as early as 2008
  • Georgia Tech PhD in Computer Engineering