LLM inference and tuning
for the enterprise.

Factual LLMs. Deployed anywhere in 10min.

Contact us
Trusted by Fortune 500 & Leading startups
Trusted by Developers
Precise recall with Lamini Memory Tuning.
Your team can achieve >95% accuracy with Lamini Memory Tuning, even with thousands of specific IDs or other internal data.
Run anywhere, including air-gapped.
Training and inference run on Nvidia or AMD GPUs in any environment — on-premise or public cloud.
Guaranteed JSON output.
By reengineering the decoder, Lamini-powered LLMs are guaranteed to output the JSON structure your apps require — with 100% schema accuracy.
Massive throughput for inference.
Lamini delivers 52x more queries per second than vLLM, so your users don’t have to wait.

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
Customer Stories

What our customers say about us

Accuracy for content classification
of manual work saved annually
Lamini's classifier SDK is easy to use... Once [the tuned LLM] was ready, we tested it, and it was so easy to deploy to production. It allowed us to move really rapidly.
Chris Lu
CTO, Copy.ai
Accuracy for text-to-SQL
of engineering time saved
Unlike sklearn, finetuning doesn’t have a lot of docs or best practices. It's a lot of trial and error, so it takes weeks to finetune a model. With Lamini, I was shocked — it was 2 hours.
Engineering leader
A fortune 100 tech company