Zhou: Lamini can train models up to 100 billion parameters
Sharon Zhou · Custom LLMs at Scale: Lamini CEO Sharon Zhou’s Playbook for Enterprise AI · Nov 8, 2023 · at 24:34
Sharon Zhou, Co-Founder and CEO of Lamini, discusses model training scale and enterprise LLM capabilities on The MAD Podcast.
0:00 / 0:02exact quote · 2.3s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“We can train up to a hundred billion parameters.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Sharon Zhou
Memory tuning eliminates hallucinations and enables near-perfect task performance
“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…”
The enterprise GPU shortage has eased at the company level
“Today, actually, I'm seeing the GPU shortage go away at the level, at the company level, meaning companies are able to procure enough compute enough is a strong word, but they're able to procure compute at some level to work with, to fine tune and run heavy in…”
In mid-2023, multi-billion dollar companies could not obtain AWS GPU nodes
“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…”
Re-engineering LLM decoders can guarantee absolute schema accuracy for structured outputs
“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.”
Future AI models will deliver 100B parameter intelligence at 1B speeds
“I even think there's a future where these models can be a hundred billion parameters, but at, you know, have that intelligence of a hundred billion parameters, but then have the speed, latency, and cost of something that's still one billion or seven billion pa…”
Combining MoE and LoRA will eliminate big versus small model trade-offs
“And I do think that's the future so we can get something that is incredibly smart, incredibly huge, but with the latency cost and speed of something, something tiny. So no more big model versus small model paradigm. It's potentially one in the same.”