OpenPangu 7B Diffusion DeepDiver — Hardware Requirements & GPU Compatibility
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OpenPangu 7B Diffusion DeepDiver is a 8.0B-parameter open language model from DLLM-Agent. It supports a context window of up to 32,768 tokens. At BF16 it needs about 16.65 GB of VRAM — see which GPUs and Macs can run it below.
125 downloads
5 likesSpecifications
- Publisher
- DLLM-Agent
- Parameters
- 8.0B
- Architecture
- PanguEmbeddedForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 153,376
- Release Date
- 2026-03-13
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HuggingFace
How Much VRAM Does OpenPangu 7B Diffusion DeepDiver Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 20.9 GB | 16.06 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run OpenPangu 7B Diffusion DeepDiver?
BF16 · 16.6 GBOpenPangu 7B Diffusion DeepDiver (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 33K context window can add up to 4.3 GB, bringing total usage to 20.9 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run OpenPangu 7B Diffusion DeepDiver?
BF16 · 16.6 GB21 devices with unified memory can run OpenPangu 7B Diffusion DeepDiver, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does OpenPangu 7B Diffusion DeepDiver need?
OpenPangu 7B Diffusion DeepDiver requires 16.6 GB of VRAM at BF16. Full 33K context adds up to 4.3 GB (20.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 16 bits ÷ 8 = 16.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.8 GB (at full 33K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context20.9 GB- Can I run OpenPangu 7B Diffusion DeepDiver on a Mac?
OpenPangu 7B Diffusion DeepDiver requires at least 16.6 GB at BF16, 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 OpenPangu 7B Diffusion DeepDiver locally?
Yes — OpenPangu 7B Diffusion DeepDiver can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OpenPangu 7B Diffusion DeepDiver?
At BF16, OpenPangu 7B Diffusion DeepDiver can reach ~175 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 16.6 × 0.55 = ~175 tok/s
Estimated speed at BF16 (16.6 GB)
~175 tok/s~39 tok/s~131 tok/s~108 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OpenPangu 7B Diffusion DeepDiver?
At BF16, the download is about 16.06 GB.
- Which GPUs can run OpenPangu 7B Diffusion DeepDiver?
6 consumer GPUs can run OpenPangu 7B Diffusion DeepDiver at BF16 (16.6 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 OpenPangu 7B Diffusion DeepDiver?
21 devices with unified memory can run OpenPangu 7B Diffusion DeepDiver at BF16 (16.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.