Tencent's Hy-MT2 Beats Microsoft's Translation API at Just 440MB

Tencent's Hunyuan team just dropped something that should make the machine translation world sit up: Hy-MT2, a family of open-source multilingual translation models that ranges from a 440MB on-device build to a 30-billion-parameter cloud flagship -- all under Apache 2.0, with no usage restrictions on commercial products, fine-tuning, or derivatives.

Hy-MT2 is billed as a family of "fast-thinking" multilingual translation models built for complex real-world scenarios, covering three sizes -- 1.8B, 7B, and 30B-A3B -- all supporting translation across 33 languages with strong instruction-following in multiple languages. The weights, quantized variants, training code, and a new evaluation benchmark called IFMTBench are all live on Hugging Face and ModelScope right now.

Three models, one story

This is not a single model release. Tencent built a tiered stack designed to cover every deployment surface from a phone to a data center.

• Hy-MT2-1.8B: The mobile-first variant. Using AngelSlim 1.25-bit extreme quantization -- a technique that compresses model weights to sub-2-bit precision -- the 1.8B model shrinks to just 440MB and runs 1.5x faster than its predecessor. It runs fully offline on Apple, Qualcomm, and MediaTek chips.
• Hy-MT2-7B: The balanced mid-tier option, optimized for server inference where you want strong quality without the compute cost of the flagship.
• Hy-MT2-30B-A3B: The flagship, built on a Mixture of Experts (MoE) architecture. MoE means the model has 30B total parameters but only activates roughly 3B of them per token -- so you get near-full-model quality at a fraction of the inference cost.

The models are optimized for various hardware platforms including ARM and Intel, and feature ultra-low-precision quantization for efficient on-device deployment. GGUF variants for llama.cpp and FP8 quantized versions for GPU inference are also available out of the box.