sarvam-translate
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve sarvamai/sarvam-translateNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
sarvam-translate is an open-weight translation model from sarvamai, published on Hugging Face in June 2025. It has 4.3B parameters and is released under the gpl-3.0 license.
HardwareLink to this section
At full FP16 precision the weights need about 9 GB of memory; 8-bit quantization brings that to roughly 5 GB. Check the VRAM tiers above to see which GPUs fit it.
PopularityLink to this section
sarvam-translate had 39,159 downloads on Hugging Face in the last month and has 164 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 sarvam-translate need?
sarvam-translate with 4.3B parameters needs approximately 2 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is sarvam-translate better than other sarvamai models?
sarvam-translate has 4.3B parameters with 131,072 context - a strong choice for translation.
What license is sarvam-translate under?
sarvam-translate is released under the gpl-3.0 license, making it suitable for most commercial and personal projects.
What hardware runs sarvam-translate well?
With 4.3B parameters, sarvam-translate 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 sarvam-translate?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~3 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can sarvam-translate's context window handle?
sarvam-translate supports a 131,072-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with sarvam-translate?
sarvam-translate 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