<@U07FRRZH2GN> fatestest llms to use from india
# ask-aia
adorable-teacher-40643
05/02/2026, 5:01 AM@icy-policeman-64898 fatestest llms to use from india
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t
tall-airline-16756
05/02/2026, 5:01 AMThanks for reaching out, Abhishek. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
tall-airline-16756
05/02/2026, 5:02 AMThanks — I’m responding to your question about the fastest LLMs to use from India.
Short answer:
• Use LLMs hosted in India (or nearby) for lowest latency — specifically Azure OpenAI deployed in India and Vertex Gemini in asia-south1 (Mumbai).
• Co-locating your voice agent and models in the same region (LiveKit Cloud Mumbai / ap-south) gives the biggest latency improvement.
If you want, tell me:
• Are you optimizing for lowest end-to-end latency, cost, or model quality?
• Which stack are you using (LiveKit Cloud or self-hosted)?
I can then recommend specific models (e.g., Gemini variants or Azure-hosted GPT models), and configuration tips (co-location, STT/TTS choices) to hit your target.
Sources: Building Performant Voice Agents in India | LiveKit | Understand and Improve Agent Latency | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit, with interactive elements
a
adorable-teacher-40643
05/02/2026, 5:04 AMoptimizing for lowest end-to-end latency, we are using livekit cloud
t
tall-airline-16756
05/02/2026, 5:04 AMThanks — responding to your goal of lowest end-to-end latency on LiveKit Cloud from India.
Recommendations (short and actionable)
• Host everything in the Mumbai region (LiveKit Cloud ap-south / asia-south1) — co-locating agent, STT, TTS, and LLMs gives the biggest latency win.
• Use region-hosted LLMs:
◦ Vertex Gemini (asia-south1 / Mumbai) — fast and available in that region.
◦ Azure OpenAI deployed in India — low-latency when deployed to an Indian region.
• Prefer lighter/faster model variants where acceptable (e.g., Gemini “Flash” / smaller GPT variants) — they return tokens faster than large base models.
Voice stack suggestions for minimal RTT
• STT: Run Deepgram (or another STT) in Mumbai.
• TTS: Use a low-latency TTS co-located in Mumbai (Cartesia or equivalent if available).
• Keep model inference and agent logic in the same region to avoid cross-region hops.
Operational tips
• Measure per-stage latency with Agent Observability (time-to-first-token, STT, inference, TTS) to find the bottleneck.
• Use streaming token responses from the LLM to start TTS earlier (reduce end-to-end time).
• Reduce audio chunk size / lower VAD latency to deliver audio to STT faster.
• Cache or use shorter prompts/state where possible to reduce model compute.
• If you need extra speed, test smaller model variants in your workload to find the best quality/latency tradeoff.
If you want, I can:
• Suggest specific model variants to test (e.g., Gemini 2.5 Flash vs. GPT-4.1 mini) and an A/B test plan.
• Provide recommended STT/TTS configs for LiveKit Cloud Mumbai.
Which would you like to do next?
, with interactive elements
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