Lamini

“The Lamini hosted service over the past year has been running on AMD GPUs only. We haven't been running on NVIDIA chips.”

“With the technology that we've used with parameter efficient fine tuning and just like efficiency, different efficiency methods, that time to switch across a thousand models is three milliseconds”

“We have reached software parity with essentially CUDA.”

“So that unlocks, what that means is that unlocks about 20,000 GPUs readily available today for enterprises to be able to use, and to get a sense of what that means you can train GBD-IV.”

“And by efficiency, I mean, you know, it's instead of something that might take weeks or even months that's bringing it down to even like the millisecond level.”

“We are the only folks who can actually run your language models on top of AMD AMD GPUs.”

“We can train up to a hundred billion parameters.”

“Been able with memory tuning, which is what I've been working on to remove those hallucinations, to remove that and actually get these models from, you know, not necessarily being general for everything. And instead of being pretty good at everything, but perf…”

“Last year was, at this time, was absolutely insane. That's why we threw up our own cloud, because there was just like, large companies with multi-billion revenue numbers could not get a node from AWS, despite their accounts being tens of millions or hundreds o…”

“We're an integrated inference and fine tuning platform for enterprises to be able to run factual LLMs. So essentially LLMs that don't hallucinate on their proprietary data within their secure walls. So we can deploy on premise air gapped, no internet sites. So…”

“So that's something else we offer through our inference service to actually make it a hundred percent by re-engineering the decoder of any LLM.”

“I believe in a future where we're continuously fine tuning these models where it's as easy as prompt engineering and you know, these models continually improve.”