Hy-MT2-1.8B
tencentapache-2.0text

Hy-MT2-1.8B

Updated Nov 13, 2026
Parameters
2B
Context
262,144
License
apache-2.0
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 Hy-MT2-1.8B with real runtime overhead.
Q4_K_M
1 GB
Q5_K_M
1 GB
Q8_0
2 GB
FP16
4 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 tencent/Hy-MT2-1.8B

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Deep dive

Notes, sources, and the full write-up

Hy-MT2-1.8B is an open-weight translation model from tencent, published on Hugging Face in May 2026. It has 2B parameters and is released under the apache-2.0 license.

HardwareLink to this section

At full FP16 precision the weights need about 4 GB of memory; 8-bit quantization brings that to roughly 2 GB. Check the VRAM tiers above to see which GPUs fit it.

PopularityLink to this section

Hy-MT2-1.8B had 22,844 downloads on Hugging Face in the last month and has 1,285 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 Hy-MT2-1.8B need?

Hy-MT2-1.8B with 2B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is Hy-MT2-1.8B better than other tencent models?

Hy-MT2-1.8B has 2B parameters with 262,144 context - a strong choice for translation.

What license is Hy-MT2-1.8B under?

Hy-MT2-1.8B is released under the apache-2.0 license, making it suitable for most commercial and personal projects.

What hardware runs Hy-MT2-1.8B well?

With 2B parameters, Hy-MT2-1.8B 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 Hy-MT2-1.8B?

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.

How long can Hy-MT2-1.8B's context window handle?

Hy-MT2-1.8B supports a 262,144-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 Hy-MT2-1.8B?

Hy-MT2-1.8B 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