Lamini
Enterprise platform to fine-tune and serve custom LLMs.
About
Lamini is an enterprise LLM platform for fine-tuning, tuning, and serving custom open models. Its Memory Tuning approach embeds factual recall directly into models via fine-tuned adapter experts, aiming to cut hallucinations on proprietary data without the brittleness of prompt-only tricks.
The platform exposes REST APIs for inference, fine-tuning jobs, classification, and embeddings, and can run in Lamini's cloud, on a customer GPU cluster, or on-premises including air-gapped environments. Founded by ML veteran Sharon Zhou and former NVIDIA CUDA architect Greg Diamos, Lamini has raised $25M and partners with AMD, Snowflake, Databricks, and Meta.
What it can do
- Memory Tuning for factual recall
- Fine-tuning jobs on open base models
- OpenAI-compatible inference API
- Cloud, on-prem, and air-gapped deployment
Setup
Create an account at lamini.ai; use the Python SDK or REST API at api.lamini.ai.
Prerequisites
API key; proprietary training data for tuning workloads.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 105; Trending score 82/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.