Memory at each precision
| Version | Weights | With context | Smallest Mac |
|---|---|---|---|
| 8-bit | 1.7 TB | 1.8 TB | more than any Mac |
| 6-bit | 1.3 TB | 1.4 TB | more than any Mac |
| 4-bit | 900 GB | 972 GB | more than any Mac |
| 3-bit lower quality | 700 GB | 756 GB | more than any Mac |
| 2-bit lower quality | 500 GB | 540 GB | more than any Mac |
On every Apple chip
The smallest memory size of each chip that runs DeepSeek V4 Pro 0813, the best version that fits it, and the writing speed you can expect.
| Chip | Needs | Version | Tokens/s |
|---|---|---|---|
| M6 | no size fits | Too big | – |
| M5 Ultra | no size fits | Too big | – |
| M5 Max | no size fits | Too big | – |
| M5 Pro | no size fits | Too big | – |
| M5 | no size fits | Too big | – |
| M4 Max | no size fits | Too big | – |
| M4 Pro | no size fits | Too big | – |
| M4 | no size fits | Too big | – |
| M3 Ultra | no size fits | Too big | – |
| M3 Max | no size fits | Too big | – |
| M3 Pro | no size fits | Too big | – |
| M3 | no size fits | Too big | – |
| M2 Ultra | no size fits | Too big | – |
| M2 Max | no size fits | Too big | – |
| M2 Pro | no size fits | Too big | – |
| M2 | no size fits | Too big | – |
| M1 Ultra | no size fits | Too big | – |
| M1 Max | no size fits | Too big | – |
| M1 Pro | no size fits | Too big | – |
| M1 | no size fits | Too big | – |
How to run it
No single Mac holds it. Smaller models below run well, and the guide to Mac clusters covers spreading a model over several Macs.
Step by step: download and run LLMs on a Mac · how to use MLX Harness · MLX vs Ollama vs llama.cpp
About DeepSeek V4 Pro 0813
| Made by | DeepSeek |
|---|---|
| Parameters | 1,600B total · 49B active (mixture of experts) |
| Context | 1M tokens |
| Licence | MIT |
| Released | 2026-08 |
| Input | Text |
| Intelligence | 36 on the Artificial Analysis Intelligence Index |
| Weights | deepseek-ai/DeepSeek-V4-Pro-0813 |
Questions
- How much memory does DeepSeek V4 Pro 0813 need?
- About 900 GB of weights at 4-bit, plus room for the context: 972 GB in all. That is more than any Mac has today.
- Can any Mac run DeepSeek V4 Pro 0813?
- Not on its own: even at 2 bits it needs more memory than the largest Mac. Use a smaller model, or spread it over several Macs with a cluster runtime.
- Why does DeepSeek V4 Pro 0813 run faster than its size suggests?
- It is a mixture-of-experts model: it holds 1,600B parameters in memory but reads only 49B for each token, so it writes at the speed of a much smaller model.
Similar models
For your model card or README
Shows the smallest Mac that runs DeepSeek V4 Pro 0813 well and links here.
[](https://mlx.app/run/deepseek-v4-pro-0813)How it’s worked out
Memory: the model’s parameters at 8, 6, 4, 3 or 2 bits per weight plus MLX’s group scales (half a bit per weight), or its native format, with room for the context on top. By default macOS lets the GPU use about 75% of unified memory. Speed: memory bandwidth divided by the bytes read per token is the ceiling for one conversation; in llama.cpp’s public Apple Silicon benchmarks real runs reach about 45% to 80% of it, which is the range shown. These are estimates, not measurements: measure your own Mac on MLX Bench. Model figures from each official model card, chip figures from Apple’s Tech Specs. Checked 2026-10-19. Same data as mlx.app/compare.