Hunyuan Large 2.0
By Tencent · China
Updated 2026-07-13
Overview
Tencent's 406B flagship MoE with 32B active parameters and 256k context. Strong on Chinese and English, but gated by the custom Tencent Hunyuan license.
When to pick this model
- Frontier Chinese-language production workloads
- Long-context RAG and document analysis up to 256k tokens
- Bilingual EN/CN enterprise deployments
- Use cases compatible with the Tencent Hunyuan license
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 245 GB |
| Q5_K_M | 290 GB |
| Q8_0 | 435 GB |
| FP16 (no quantization) | 810 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Hunyuan Large 2.0 is server-class even at Q4_K_M (245 GB). Stepping up to Q8_0 nearly doubles the footprint to 435 GB, and unquantized FP16 weights take 810 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Hunyuan Large 2.0 needs roughly 300 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 4 tokens/sec on a mid-range card, and up to 12 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Hunyuan Large 2.0 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Hunyuan Large 2.0 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 245 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 245 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 245 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 245 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 245 GB at Q4_K_M |
Which GPU should you buy to run Hunyuan Large 2.0?
To run Hunyuan Large 2.0 locally at Q4, you need ~245 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- 262k native context window
- Top-tier Chinese-language performance
- Efficient inference relative to 406B total size
- Strong on RAG and long-document tasks
Limitations
- Around 245 GB VRAM at Q4 — heavy infrastructure needed
- Custom Tencent license requires careful legal review
- Limited adoption outside Chinese-speaking markets
Typical workloads
In our catalog grid, Hunyuan Large 2.0 is filed under CN Frontier, Long Context — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 256k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Tencent Hunyuan License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: MoE · 406B total / 52B active · Tencent HunyuanLLM · 262k ctx
Training: Tencent — strong in Chinese and English, RAG and long document.
Frontier-class bilingual long-context performance, but licensing and infrastructure demands narrow its practical audience.
Quick start
# Infrastructure Tencent — non disponible en local standardOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Hunyuan Large 2.0 need?
At the recommended Q4_K_M quantization, Hunyuan Large 2.0 needs about 245 GB of VRAM. Q8_0 takes 435 GB, and unquantized FP16 weights take 810 GB.
Can Hunyuan Large 2.0 run without a GPU?
Yes — with roughly 300 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Hunyuan Large 2.0 support?
Hunyuan Large 2.0 supports a 256k-token context window (262,144 tokens).
Can I use Hunyuan Large 2.0 commercially?
Hunyuan Large 2.0 ships under the Tencent Hunyuan License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Hunyuan Large 2.0 on consumer hardware?
Our compatibility engine estimates on the order of 4 tokens/sec on a mid-range GPU and up to 12 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Hunyuan Large 2.0 should I download first?
Start with Q4_K_M (245 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.