Hy MT2 30B A3B — Hardware Requirements & GPU Compatibility
Translation
Hy MT2 30B A3B is a 30.1B-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 18.44 GB of VRAM — see which GPUs and Macs can run it below.
188.0K downloads
475 likesSpecifications
- Publisher
- Tencent
- Family
- Hunyuan MT2
- Parameters
- 30.1B
- Architecture
- HYV3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 120,832
- Release Date
- 2026-05-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Hy MT2 30B A3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 13.2 GB | 26.0 GB | 12.78 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.6 GB | 26.3 GB | 13.15 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 15.1 GB | 27.8 GB | 14.66 GB | 3-bit medium quantization |
| Q4_K_S | 4.50 | 17.3 GB | 30.1 GB | 16.91 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 18.4 GB | 31.2 GB | 18.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 21.1 GB | 33.9 GB | 20.67 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 21.8 GB | 34.6 GB | 21.42 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 25.2 GB | 38.0 GB | 24.80 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 30.5 GB | 43.3 GB | 30.06 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 60.5 GB | 73.3 GB | 60.13 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Hy MT2 30B A3B?
Q4_K_M · 18.4 GBHy MT2 30B A3B (Q4_K_M) requires 18.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 31.2 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Hy MT2 30B A3B?
Q4_K_M · 18.4 GB41 devices with unified memory can run Hy MT2 30B A3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Hy MT2 30B A3B
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 30B A3B need?
Hy MT2 30B A3B requires 18.4 GB of VRAM at Q4_K_M, or 60.5 GB at BF16. Full 262K context adds up to 12.8 GB (31.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 30.1B × 4.8 bits ÷ 8 = 18 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M18.4 GBQ4_K_M + full context31.2 GB- Can NVIDIA GeForce RTX 4090 run Hy MT2 30B A3B?
Yes, at Q5_K_M (21.8 GB) or lower. Higher quantizations like Q6_K (25.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Hy MT2 30B A3B?
For Hy MT2 30B A3B, Q4_K_M (18.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (21.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 13.2 GB.
VRAM requirement by quantization
Q2_K13.2 GBQ3_K_M15.1 GBQ4_K_M ★18.4 GBQ5_K_S21.1 GBQ6_K25.2 GBBF1660.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Hy MT2 30B A3B on a Mac?
Hy MT2 30B A3B requires at least 13.2 GB at Q2_K, 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 30B A3B locally?
Yes — Hy MT2 30B A3B can run locally on consumer hardware. At Q4_K_M quantization it needs 18.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hy MT2 30B A3B?
At Q4_K_M, Hy MT2 30B A3B can reach ~239 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~36 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 ÷ 18.4 × 0.65 = ~282 tok/s
Estimated speed at Q4_K_M (18.4 GB)
~282 tok/s~36 tok/s~282 tok/s~239 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 30B A3B?
At Q4_K_M, the download is about 18.04 GB. The full-precision BF16 version is 60.13 GB. The smallest option (Q2_K) is 12.78 GB.
- Which GPUs can run Hy MT2 30B A3B?
8 consumer GPUs can run Hy MT2 30B A3B at Q4_K_M (18.4 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Hy MT2 30B A3B?
41 devices with unified memory can run Hy MT2 30B A3B at Q4_K_M (18.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.