Can DeepSeek V4 Flash Vision Exp run on Mac Studio M5 Ultra 256GB?
What this means
DeepSeek V4 Flash Vision Exp fits on the Mac Studio M5 Ultra · 256GB at Q4_K_XL. We estimate it uses 183.5GB of the conservative 248GB working budget.
Estimated decode speed is 84.5–118.3 tokens/s. A roughly 300-word answer may take around 4 seconds.
See how fast it feels
Using the midpoint of our 84.5–118.3 tokens/s estimate for this demo.
Where the memory goes
| Component | Detail | GB |
|---|---|---|
| Model weights | Q4_K_XL GGUF (155.1 GB) + mmap overhead | 162.9 |
| KV cache | 4K context window | 19.9 |
| Runtime | macOS inference app + compute buffers | 0.8 |
| Total needed | at Q4_K_XL, 4K context | 183.5 |
| Working budget | 256 GB unified memory − conservative macOS reserve | 248 |
| Headroom | remaining inside the working budget | 64.5 |
Pick your quant
| Quant | Download | Memory | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4_K_XL ★ | 155.1 GB | 183.5 GB | ~84.5–118.3 tok/s | ✓ Runs great |
| Q8_K_XL | 161.9 GB | 190.7 GB | ~81–113.3 tok/s | ✓ Runs great |
Get it running on this Mac
Recommended app: LM Studio. Follow the point-and-click steps below.
Download LM Studio from its official site and drag it to Applications. Open it once and allow macOS to launch it. This route uses the graphical app; you do not need its CLI or local-server features.
Search for unsloth/DeepSeek-V4-Flash-Vision-Exp-GGUF. Use the publisher name shown on this page—or paste its full Hugging Face URL. Similarly named community uploads may contain different files.
Open the GGUF download options and select the row containing Q4_K_XL. The download should be about 155.1 GB. Do not select vision-projector or mmproj helper files for a text-only chat.
Choose the downloaded model in the model selector and click Load if prompted. Keep the default local runtime and start with a 4K (4096-token) context. Close memory-heavy apps for the first load.
Try “Explain why the sky is blue in three sentences.” This page estimates 84.5–118.3 tokens/s; it is not an LM Studio measurement unless marked ✓ Verified.
A second reply while offline confirms that the model is running on this Mac.
This report names an exact Hugging Face repository and GGUF quant. LM Studio lets you search that exact source, choose the matching quant, download it, and chat without using Terminal.
An open-source GGUF alternative with friendly hardware-fit hints.
Its MLX engine is experimental, and support for new model architectures can lag.A trusted Ollama catalog model, or later use with coding tools and a local API.
An Ollama package may use different weights or a different quant from this report.One interface for GGUF, MLX, Ollama, documents, and knowledge workflows.
It exposes more choices than the first-chat path and its free license is for personal use.- Model not listed: paste the exact repository unsloth/DeepSeek-V4-Flash-Vision-Exp-GGUF, not only the model nickname.
- Download stalls: confirm there is enough free storage, reconnect to Wi-Fi, and restart the download inside the app.
- Load fails or the app closes: quit memory-heavy apps. In LM Studio, confirm Q4_K_XL is the model currently loaded. Otherwise use a smaller model from this Mac's results.
- No reply while offline: make sure the downloaded local model—not a remote or cloud model—is selected in the chat.
Your anonymous feedback helps us prioritize which Mac and model paths to retest.
Other models on this Mac
DeepSeek V4 Flash Vision Exp on other Mac Studio M5 Ultra configurations
FAQ
Can the Mac Studio M5 Ultra · 256GB run DeepSeek V4 Flash Vision Exp?
Yes at Q4_K_XL. We estimate about 183.5GB of working memory and 84.5–118.3 tokens/s at 4K context.
Which DeepSeek V4 Flash Vision Exp quant should I use on this Mac?
Q4_K_XL. It is a 155.1GB download and leaves about 64.5GB inside our conservative working budget.
Which app should I use for DeepSeek V4 Flash Vision Exp on this Mac?
Start with LM Studio. This page gives the complete point-and-click walkthrough.
Are these speeds measured on a Mac Studio M5 Ultra?
No. The range is a formula estimate based on memory bandwidth, model size, active parameters, cooling, and a 4K context. The app, backend, thermals, and prompt can change real performance.