indictrans2-en-indic-1B
ai4bharatmittext

indictrans2-en-indic-1B

Updated Nov 13, 2026
Parameters
1.1B
Context
8,192
License
mit
Updated
Nov 13, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit indictrans2-en-indic-1B with real runtime overhead.
Q4_K_M
1 GB
Q5_K_M
1 GB
Q8_0
1 GB
FP16
2 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve ai4bharat/indictrans2-en-indic-1B

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

indictrans2-en-indic-1B is an open-weight translation model from ai4bharat, published on Hugging Face in September 2023. It has 1.1B parameters and is released under the mit license.

HardwareLink to this section

At full FP16 precision the weights need about 2 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.

PopularityLink to this section

indictrans2-en-indic-1B had 48,075 downloads on Hugging Face in the last month and has 91 likes.

Run it locallyLink to this section

The weights are on Hugging Face and load with Hugging Face Transformers. Follow the model card for the exact setup and license terms.

Frequently asked

Quick answers to common questions

How much VRAM does indictrans2-en-indic-1B need?

indictrans2-en-indic-1B with 1.1B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is indictrans2-en-indic-1B better than other ai4bharat models?

indictrans2-en-indic-1B has 1.1B parameters with 8,192 context - a strong choice for translation.

What license is indictrans2-en-indic-1B under?

indictrans2-en-indic-1B is released under the mit license, making it suitable for most commercial and personal projects.

What hardware runs indictrans2-en-indic-1B well?

With 1.1B parameters, indictrans2-en-indic-1B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for indictrans2-en-indic-1B?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

What models compete with indictrans2-en-indic-1B?

indictrans2-en-indic-1B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec