Z Image Engineer V6 — Hardware Requirements & GPU Compatibility
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Z Image Engineer V6 is a 4.0B-parameter open language model from BennyDaBall. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 2.90 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 40K)Best on 24 GB (Q4_K_M · ~226 tok/s · 40K)Macs from 8 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Z Image Engineer V6
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/Z-Image-Engineer-V6-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.8 GB free · 16K context
Download: Z-Image-Engineer-V6.Q4_K_M.gguf2.50 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~81 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does Z Image Engineer V6 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.2 GB | 5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~298 tok/s | |
| 2.3 GB | 5.1 GB | 3-bit small — 3-bit small quantization | Fits: 40K context · ~291 tok/s | |
| 2.5 GB | 5.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~267 tok/s | |
| 2.5 GB | 5.4 GB | 3-bit large — 3-bit large quantization | Fits: 40K context · ~257 tok/s | |
| 2.9 GB | 5.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 40K context · ~226 tok/s | |
| 3.4 GB | 6.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 40K context · ~196 tok/s | |
| 3.8 GB | 6.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 40K context · ~172 tok/s | |
| 4.5 GB | 7.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 40K context · ~145 tok/s |
Selected Q4_K_M
4-bit medium quantization4.2GBat 16K context
- Weights
- 2.4 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.8 GB free · ~226 tok/s
≥50 tok/s needs ≥223 GB/s · RTX 5090 ~402, RTX 3090 Ti ~226
Which Hardware Can Run Z Image Engineer V6?
Q4_K_M · 2.9 GBZ Image Engineer V6 (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 41K context window can add up to 3.6 GB, bringing total usage to 6.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Z Image Engineer V6, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.8 GB free · up to 40K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~170Simulate output on RTX 3080RTX 2080 Ti~138Simulate output on RTX 2080 TiRTX 3070 Ti~136Simulate output on RTX 3070 TiM3*8 GB~25Simulate output on iMac 24" M3 (8 GB)Orin Nano~23Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~17Simulate output on iPhone 1720 GB
fits · 15.8 GB free · up to 40K ctx
1 GPU
RX 7900 XT~166Simulate output on RX 7900 XT32–40 GB
fits · 27.8 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~402Simulate output on RTX 5090M5 Max36 GB~111Simulate output on Mac Studio M5 Max (36 GB)M532 GB~37Simulate output on MacBook Air 13" M5 (32 GB)Orin~46Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~30Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
Specifications
- Publisher
- BennyDaBall
- Parameters
- 4.0B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-06-06
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Z Image Engineer V6 need?
Z Image Engineer V6 requires 2.9 GB of VRAM at Q4_K_M, or 8.5 GB at BF16. Full 41K context adds up to 3.6 GB (6.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 4.1 GB (at full 41K context)
VRAM usage by quantization
What's the best quantization for Z Image Engineer V6?
For Z Image Engineer V6, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Z Image Engineer V6 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Z Image Engineer V6. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Z Image Engineer V6 locally?
Yes — Z Image Engineer V6 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Z Image Engineer V6?
At Q4_K_M, Z Image Engineer V6 can reach ~1655 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~226 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 ÷ 2.9 × 0.65 = ~1793 tok/s
Estimated speed at Q4_K_M (2.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Z Image Engineer V6?
At Q4_K_M, the download is about 2.41 GB. The full-precision BF16 version is 8.04 GB. The smallest option (Q2_K) is 1.71 GB.
Which GPUs can run Z Image Engineer V6?
52 consumer GPUs can run Z Image Engineer V6 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Z Image Engineer V6?
163 devices with unified memory can run Z Image Engineer V6 at Q4_K_M (2.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.