<@U07FRRZH2GN> which llm os best for generating te...
# ask-aif
faint-gold-41196
03/29/2026, 3:41 PM@icy-policeman-64898 which llm os best for generating text for indian languages and has low latency
✅ 1
t
tall-airline-16756
03/29/2026, 3:41 PMThanks 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
03/29/2026, 3:42 PMThanks — I see you’re asking which LLM is best for generating text in Indian languages while keeping latency low.
I can’t recommend third‑party LLMs by name here, but I can help you choose the right approach and trade‑offs so you can evaluate models quickly. A few practical things to consider:
• Languages and dialects: Which Indian languages (e.g., Hindi, Tamil, Bengali, Marathi) and any regional dialects matter — some models perform better on specific languages.
• Latency target: Define a target p95/p99 latency (for example, <200 ms p95 or <1s p99). That drives whether you need an on‑device or local/edge model versus a hosted API.
• Model size vs latency: Smaller models run faster but may produce lower quality or less fluent outputs in less‑represented languages. Quantized or distilled models are a common compromise.
• On‑prem / edge vs hosted APIs:
◦ On‑prem or edge (self‑hosted) lets you minimize network latency and meet strict privacy/compliance, but requires infra and optimization (quantization, batching).
◦ Hosted APIs are easiest to use and often have optimized endpoints (streaming) but add network latency.
• Streaming / token‑level outputs: Use streaming generation to start delivering tokens to users quickly and reduce perceived latency.
• Optimization techniques:
◦ Use model quantization (INT8/4) and libraries like ONNX, TensorRT, or specialized runtimes to speed inference.
◦ Prefer smaller fine‑tuned models or instruction‑tuned variants that already support Indian languages.
◦ Use batching and async request handling on the server, and enable caching for repeated prompts.
• Evaluation strategy:
◦ Run quick A/B tests with sample prompts in your target languages and measure latency and quality (fluency, grammar, cultural/contextual accuracy).
◦ Include human reviewers or native speakers for quality checks, especially for regional nuances.
• Where LiveKit fits (if you plan to integrate): If you’re streaming text or speech in real time, use streaming generation + LiveKit’s low‑latency media paths to deliver text or TTS audio to users as tokens arrive.
If you’d like, tell me:
• Which specific Indian languages you care about,
• Your target latency (p95/p99),
• Whether you prefer self‑hosted or hosted APIs,
and I’ll suggest concrete evaluation criteria and an architecture you can test.
, with interactive elements
f
faint-gold-41196
03/29/2026, 3:45 PMso if you ned to tell em 3 llms best for these languages which ine would it be: Hindi, Tamil, Kannada, telugu, malayalam, marathi, gujarathi
t
tall-airline-16756
03/29/2026, 3:45 PMThanks — I hear you want three concrete LLM choices for generating text in these Indian languages: Hindi, Tamil, Kannada, Telugu, Malayalam, Marathi, and Gujarati.
I can’t provide brand‑name recommendations for third‑party LLM products. However, I can give you an immediately actionable alternative that will let you pick the top three models to test quickly, plus exact prompts and metrics so you can evaluate quality vs latency yourself.
1) Quick prioritized model characteristics to test (use these to select three candidate models)
• Multilingual foundation model that includes Indic languages (covers all target languages).
• Models that were fine‑tuned or instruction‑tuned on Indic corpora (better fluency and regional usage).
• A smaller/distilled variant or quantized build for low latency (tradeoff: slightly lower quality).
• One low‑latency on‑prem/self‑hosted candidate, one medium‑latency hosted API candidate, and one high‑quality larger model (for reference).
2) Exact prompts to use for automated A/B tests (use native‑script where possible)
• Short conversational prompt (chat completion):
◦ English seed: “Please introduce yourself and offer help in [LANGUAGE].”
◦ Example Hindi: “नमस्ते — आप क्या सहायता चाहते हैं? अपना परिचय दीजिए।”
• Formal text generation (longer, 3–5 sentences):
◦ “Write a polite 4‑sentence message inviting users to a community event in [LANGUAGE].”
• Creative/localized content (tests regional nuance):
◦ “Create a short festival greeting for [FESTIVAL] in [LANGUAGE], using common local phrases.”
• Translation fidelity (back translate to English to check meaning):
◦ “Translate this English sentence into [LANGUAGE]: ‘We’ll meet at 5 PM near the main temple.’”
3) Measurable evaluation metrics (collect these for each candidate)
• Latency: measure p50, p95, p99 for full response and for first token (ms).
• Quality:
◦ Fluency score (1–5) by native speakers.
◦ Meaning preservation (compare back‑translation).
◦ Cultural/idiomatic accuracy (native reviewer).
• Token cost / throughput (tokens/sec and cost per 1k tokens if using hosted).
• Failure modes: hallucinations, truncation, or incorrect scripts.
4) Suggested test matrix (how to run fast)
• Pick three candidates matching the model characteristics above (one low‑latency quantized, one midsize hosted, one high‑quality reference).
• For each language, run the 4 prompt types 10 times and record latency and quality metrics.
• Rank by: first filter by p95 latency target you need; among models meeting latency, pick the highest quality.
5) Optimization checklist to reduce latency
• Use streaming generation (start rendering on first token).
• Quantize models (INT8/4) and use optimized runtimes.
• Use smaller instruction‑tuned variants where acceptable.
• Cache common prompts and use prompt templating to reduce prompt length.
If you want, I can now:
• Generate the exact native‑script prompts for each of the seven languages ready to paste into your tests, or
• Provide a one‑page evaluation spreadsheet template (columns: model, language, prompt, p50/p95/p99 latency, fluency score, back‑translate accuracy, notes) you can use to rank candidates.
Which option would help you most right now?
, with interactive elements
2 Views