The seeds were sown through a consortium of academic institutions.

IIT Bombay took the lead,

we went through a call for proposals

and we were selected to build

the small models up to 10 billion parameters,

with 235 crore funding,

over a period of two years.

We applied to the India AI Mission,

for building models up to 1 trillion parameters.

The proposal was looked upon very favorably,

and the whole initiative grew into,

completely government owned section

We just had a whole bunch of models

launched with the honorable PM,

multilingual across 12 languages, the speech models

and 22 languages, text models,

and we're very happy with the collaborations

that have also grown around this.

With hospitals,

with state governments,

with the several line departments

and departments of water and sanitation,

How can we use our workload?

with healthcare making it easily comprehensible by the patient,

with healthcare making it easily comprehensible by the patient,

making it possible for an ordinary person to learn other languages

and communicate in that language.

I saw so many interesting use cases of GyanBharatam for example.

There have been lots of interesting engagement with finance sector as well,

and even in the government space, education is a huge potential.

Work we been doing with Kotak Education Foundation and spoken tutorials.

Giving them both co-pilots,

that will help them converse better

and be more effective in their assessments.

So we use them as catalysts, as very good learning points.

NVIDIA has been a solid partner in that entire journey.

We went through many cycles.

We started Megatron, Nemotron.

We did find a very healthy

ecosystem building around Nemotron

So we are grateful for that, especially on the pre-training and post-training part.

You know, open source, which is what NVIDIA stands for, right?

Open source is not just the code,

but is also the dialog.

We are very grateful to NVIDIA for helping us scale our model to multiple nodes.

But again, it's not just scaling to multiple nodes.

How is it being scaled?

How do you maintain good efficiency and throughput.

So observability comes very handy there.

The development, flexibility and scalability

has been very important

learning for us in our journey together with NVIDIA.

The BCM and Slurm,

via NCP,

Nemo 2.0 for the pre-training,

NemoRL for the post training

are very specific examples of how we have leveraged

the open source ecosystem that NVIDIA has been creating

and while that is very important in terms of training,

there's also other important aspect,

which is inference.

We are in conversations with NVIDIA on how we could do the inference.

We'll continue working together,

build large models.

Anyone who stands for open source is,

partner for life.

So we like to work with NVIDIA’s open source initiatives.

And as I said,

it triggers both in-house capability,

avoids re-invention of it.

And I think contributes to the community as a whole.