Pangu Pro MoE 72B
By Huawei · China
Updated 2026-07-13
Overview
Huawei's first open-weight release, a 72B MoE optimized for Ascend silicon. Strong on enterprise code and Chinese business scenarios, but the custom Pangu license needs careful review.
When to pick this model
- Deployments already running on Huawei Ascend hardware
- Enterprise code and business workflows in Chinese markets
- Research on non-NVIDIA training and inference stacks
- Workloads where Huawei's ecosystem integration matters
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 42 GB |
| Q5_K_M | 50 GB |
| Q8_0 | 78 GB |
| FP16 (no quantization) | 144 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, Pangu Pro MoE 72B spills past single consumer GPUs even at Q4_K_M (42 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 nearly doubles the footprint to 78 GB, and unquantized FP16 weights take 144 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Pangu Pro MoE 72B needs roughly 72 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 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 28 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Pangu Pro MoE 72B 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 Pangu Pro MoE 72B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 42 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 42 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 42 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 42 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 42 GB at Q4_K_M |
Which GPU should you buy to run Pangu Pro MoE 72B?
To run Pangu Pro MoE 72B locally at Q4, you need ~42 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- First-class optimization for Ascend NPUs
- Solid enterprise code and business reasoning
- Open weights from a major hyperscaler
- MoE design keeps inference tractable
Limitations
- Around 42 GB VRAM in Q4
- 32k context trails modern flagships
- Custom Pangu license requires legal review
- Tooling outside Huawei's stack is thin
Typical workloads
In our catalog grid, Pangu Pro MoE 72B is filed under Ascend Chips, CN Enterprise — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Pangu Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: MoE · 72B · Huawei PanGu Pro · proprietary architecture
Training: Huawei — specialized in enterprise code and CN business scenarios.
A reasonable pick if you're on Ascend; on NVIDIA hardware, Qwen 3.5 or DeepSeek will serve you better.
Quick start
ollama pull hf.co/huawei/pangu-pro-moe-72b-GGUFOr 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 Pangu Pro MoE 72B need?
At the recommended Q4_K_M quantization, Pangu Pro MoE 72B needs about 42 GB of VRAM. Q8_0 takes 78 GB, and unquantized FP16 weights take 144 GB.
Can Pangu Pro MoE 72B run without a GPU?
Yes — with roughly 72 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 Pangu Pro MoE 72B support?
Pangu Pro MoE 72B supports a 32k-token context window (32,768 tokens).
Can I use Pangu Pro MoE 72B commercially?
Pangu Pro MoE 72B ships under the Pangu Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Pangu Pro MoE 72B on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 28 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Pangu Pro MoE 72B should I download first?
Start with Q4_K_M (42 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.