LLM inference and tuning
for the enterprise.
Factual LLMs. Deployed anywhere in 10min.
Trusted by Fortune 500 & Leading startups
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
Customer Stories
What our customers say about us
100%
Accuracy for content classification
1200+h
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
94.7%
Accuracy for text-to-SQL
100+h
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
Blogs
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