Meta's Llama models are the most widely deployed open-weight family in the world, and their multilingual reasoning holds up well on Indian calls. Run them managed on Vomyra, on Groq for speed, or on your own hardware.
Llama is Meta's family of open-weight large language models, now through Llama 4 alongside the widely deployed Llama 3.x line. Because the weights are published, Llama can be served by any inference provider or run on your own hardware — which has made it the default foundation for teams that want frontier-adjacent capability without a proprietary vendor dependency.
Llama gives you two things at once that usually trade off: multilingual reasoning strong enough for Indian-language calls, and an economic profile that survives a hundred thousand calls a month. Served on Groq's LPUs it is also among the fastest options available, which matters more on a phone call than on a chat interface.
What you get, stated plainly enough to check.
Four steps, none of which involve writing telephony code.
Select the size that matches the task. Qualification agents rarely need the largest variant available.
Managed on Vomyra, served on Groq for the lowest latency, or self-hosted inside your own infrastructure.
Add Cartesia or ElevenLabs for speech and an Indian mobile-format number so the call gets answered.
Open weights mean cost per call stays predictable as volume grows, rather than scaling with a proprietary per-token rate.
At a lakh of calls a month, open-weight economics is often the difference between a campaign that runs continuously and one that runs in bursts.
Published weights mean you can move between inference providers, or bring it in-house, without rewriting the agent.
Llama's multilingual reasoning handles Hindi, English and code-mixed conversation well when paired with the right voice.
Yes. On Vomyra, Llama is selectable as the reasoning model behind any voice agent running on Indian telephony, and its multilingual reasoning handles Hindi, English and code-mixed calls well when paired with an appropriate voice model.
Cost at scale and portability. Open weights keep per-call economics predictable across a hundred thousand calls a month, and because the weights are published you can move between inference providers or bring it in-house without rewriting the agent.
Yes, on enterprise plans. Self-hosting is the usual route for government, public-sector and regulated deployments where call content cannot leave a controlled perimeter.
Served on Groq's LPU inference. Generation speed is directly audible as the silence after a caller stops talking, and Groq is the clearest lever on that number for open-weight models.
It performs solidly on Hindi and code-mixed conversation. For Hindi-first campaigns where prosody and interruption handling are central, AWS Nova 2 Sonic's native speech-to-speech Hindi is typically the stronger option — and both are switchable on the same agent.