Z Image Turbo — Hardware Requirements & GPU Compatibility
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Z Image Turbo is a 6.2B-parameter open language model from Tongyi-MAI. At BF16 it needs about 13.54 GB of VRAM — see which GPUs and Macs can run it below.
682.3K downloads
5.2K likesSpecifications
- Publisher
- Tongyi-MAI
- Parameters
- 6.2B
- Release Date
- 2025-11-25
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Z Image Turbo Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 13.5 GB | — | 12.31 GB | Brain floating point 16 — preferred for training |
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 Z Image Turbo?
BF16 · 13.5 GBZ Image Turbo (BF16) requires 13.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 18+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Z Image Turbo?
BF16 · 13.5 GB47 devices with unified memory can run Z Image Turbo, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Z Image Turbo need?
Z Image Turbo requires 13.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.2B × 16 bits ÷ 8 = 12.3 GB
KV Cache + Overhead ≈ 1.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1613.5 GB- Can I run Z Image Turbo on a Mac?
Z Image Turbo requires at least 13.5 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 Z Image Turbo locally?
Yes — Z Image Turbo can run locally on consumer hardware. At BF16 quantization it needs 13.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Z Image Turbo?
At BF16, Z Image Turbo can reach ~355 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~48 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 ÷ 13.5 × 0.65 = ~384 tok/s
Estimated speed at BF16 (13.5 GB)
~384 tok/s~48 tok/s~384 tok/s~355 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Z Image Turbo?
At BF16, the download is about 12.31 GB.
- Which GPUs can run Z Image Turbo?
26 consumer GPUs can run Z Image Turbo at BF16 (13.5 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Z Image Turbo?
49 devices with unified memory can run Z Image Turbo at BF16 (13.5 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.