Tencent·Hunyuan 3·HYV3ForCausalLM

Hy3 Preview — Hardware Requirements & GPU Compatibility

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Hy3 Preview is a 298.8B-parameter open language model from Tencent in the Hunyuan 3 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 179.91 GB of VRAM — see which GPUs and Macs can run it below.

73.5K downloads 300 likes262K context

Specifications

Publisher
Tencent
Family
Hunyuan 3
Parameters
298.8B
Architecture
HYV3ForCausalLM
Context Length
262,144 tokens
Vocabulary Size
120,832
Release Date
2026-04-13
License
Other

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How Much VRAM Does Hy3 Preview Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.40127.6 GB
Q3_K_Mest.3.90146.3 GB
Q4_K_Mest.4.80179.9 GB
Q5_K_Mest.5.70213.5 GB
Q6_Kest.6.60247.1 GB
Q8_0est.8.00299.4 GB
BF16est.16.00598.2 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run Hy3 Preview?

Q4_K_M · 179.9 GB

Hy3 Preview (Q4_K_M) requires 179.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 234+ GB is recommended. Using the full 262K context window can add up to 42.6 GB, bringing total usage to 222.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Hy3 Preview?

Q4_K_M · 179.9 GB

6 devices with unified memory can run Hy3 Preview, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).

Frequently Asked Questions

How much VRAM does Hy3 Preview need?

Hy3 Preview requires 179.9 GB of VRAM at Q4_K_M, or 598.2 GB at BF16. Full 262K context adds up to 42.6 GB (222.5 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 298.8B × 4.8 bits ÷ 8 = 179.3 GB

KV Cache + Overhead 0.6 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 43.2 GB (at full 262K context)

VRAM usage by quantization

179.9 GB
222.5 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Hy3 Preview?

No — Hy3 Preview requires at least 127.6 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Hy3 Preview?

For Hy3 Preview, Q4_K_M (179.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (213.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 127.6 GB.

VRAM requirement by quantization

Q2_K
127.6 GB
Q4_K_M
179.9 GB
Q5_K_M
213.5 GB
Q6_K
247.1 GB
Q8_0
299.4 GB
BF16
598.2 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Hy3 Preview on a Mac?

Hy3 Preview requires at least 127.6 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 Hy3 Preview locally?

Yes — Hy3 Preview can run locally on consumer hardware. At Q4_K_M quantization it needs 179.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Hy3 Preview?

At Q4_K_M, Hy3 Preview can reach ~25 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B2008000 ÷ 179.9 × 0.65 = ~29 tok/s

Estimated speed at Q4_K_M (179.9 GB)

~29 tok/s
~29 tok/s
~25 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Hy3 Preview?

At Q4_K_M, the download is about 179.27 GB. The full-precision BF16 version is 597.57 GB. The smallest option (Q2_K) is 126.98 GB.

Which GPUs can run Hy3 Preview?

No single consumer GPU has enough VRAM to run Hy3 Preview at Q4_K_M (179.9 GB). Multi-GPU or professional hardware is required.

Which devices can run Hy3 Preview?

6 devices with unified memory can run Hy3 Preview at Q4_K_M (179.9 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.