Hy MT2 1.8B — Hardware Requirements & GPU Compatibility
Translation
Hy MT2 1.8B is a 2.0B-parameter open language model from Tencent in the Hunyuan MT2 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 1.66 GB of VRAM — see which GPUs and Macs can run it below.
155.7K downloads
1.1K likesSpecifications
- Publisher
- Tencent
- Family
- Hunyuan MT2
- Parameters
- 2.0B
- Architecture
- HunYuanDenseV1ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 120,818
- Release Date
- 2026-05-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Hy MT2 1.8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.3 GB | 18.4 GB | 0.87 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.3 GB | 18.4 GB | 0.89 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.4 GB | 18.5 GB | 0.99 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.4 GB | 18.5 GB | 1.02 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.7 GB | 18.7 GB | 1.22 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.9 GB | 18.9 GB | 1.45 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.1 GB | 19.2 GB | 1.68 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.5 GB | 19.5 GB | 2.04 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Hy MT2 1.8B?
Q4_K_M · 1.7 GBHy MT2 1.8B (Q4_K_M) requires 1.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 262K context window can add up to 17.0 GB, bringing total usage to 18.7 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Hy MT2 1.8B?
Q4_K_M · 1.7 GB59 devices with unified memory can run Hy MT2 1.8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Hy MT2 1.8B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Hy MT2 1.8B need?
Hy MT2 1.8B requires 1.7 GB of VRAM at Q4_K_M, or 4.5 GB at BF16. Full 262K context adds up to 17.0 GB (18.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.0B × 4.8 bits ÷ 8 = 1.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M1.7 GBQ4_K_M + full context18.7 GB- What's the best quantization for Hy MT2 1.8B?
For Hy MT2 1.8B, Q4_K_M (1.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 1.1 GB.
VRAM requirement by quantization
IQ2_M1.1 GBQ3_K_M1.4 GBQ4_11.6 GBQ4_K_M ★1.7 GBQ5_K_S1.8 GBBF164.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Hy MT2 1.8B on a Mac?
Hy MT2 1.8B requires at least 1.1 GB at IQ2_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Hy MT2 1.8B locally?
Yes — Hy MT2 1.8B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hy MT2 1.8B?
At Q4_K_M, Hy MT2 1.8B can reach ~2651 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~395 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.7 × 0.65 = ~3133 tok/s
Estimated speed at Q4_K_M (1.7 GB)
~3133 tok/s~395 tok/s~3133 tok/s~2651 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Hy MT2 1.8B?
At Q4_K_M, the download is about 1.22 GB. The full-precision BF16 version is 4.08 GB. The smallest option (IQ2_M) is 0.69 GB.
- Which GPUs can run Hy MT2 1.8B?
50 consumer GPUs can run Hy MT2 1.8B at Q4_K_M (1.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Hy MT2 1.8B?
59 devices with unified memory can run Hy MT2 1.8B at Q4_K_M (1.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.