LLM inference and tuning
for the enterprise.
Factual LLMs. Up in 10min. Deployed anywhere.
Our happy customers
Trusted by developers
Product
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
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