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text model · DeepSeek-V4 · macOS

Can I run DeepSeek-V4-Pro on Apple M5 Pro (48GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. DeepSeek-V4-Pro needs ~965 GB even at Q4_K_M, but Apple M5 Pro (48GB) only has ~32 GB usable.

Needs ~965 GB Device usable ~32 GB

Needs ~965 GB even at Q4_K_M, but only ~32 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 933 GB: DeepSeek-V4-Pro needs roughly 965 GB at Q4_K_M and Apple M5 Pro (48GB) leaves only about 32 GB usable for a model. No single tracked device has enough memory; DeepSeek-V4-Pro needs a multi-GPU or high-memory rig.

Q4_K_M needed
~965 GB
Usable on device
~32 GB
Device memory
48 GB
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Which quant fits

Quant ladder vs ~32 GB usable
Q2_K
~680.4 GB
Q3_K_M
~792.4 GB
Q4_K_M
~965 GB
Q5_K_M
~1150.4 GB
Q6_K
~1322.4 GB
Q8_0
~1682.2 GB
FP16
~3210.4 GB
The line marks Apple M5 Pro (48GB)'s ~32 GB budget; rungs past it are too large.

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model DeepSeek-V4
Parameters
1600B (MoE, 49B active)
Q4_K_M size
954.58 GB
Q8_0 size
1671.82 GB
Context
1000k
Full DeepSeek-V4-Pro requirements →
Device macOS
Memory
48 GB unified
Usable for weights
~32 GB
Best runtime
MLX direct / Ollama (MLX backend)
Best models for Apple M5 Pro (48GB) →

What you can run instead

FAQ

Can Apple M5 Pro (48GB) run DeepSeek-V4-Pro?

No. DeepSeek-V4-Pro needs ~965 GB even at Q4_K_M, but Apple M5 Pro (48GB) only has ~32 GB usable.

How much memory does DeepSeek-V4-Pro need?

Apple M5 Pro (48GB) does not have enough memory. At Q4_K_M the weights are ~954.58 GB; with KV cache and runtime overhead, budget ~965 GB at a 4k context. It is a Mixture-of-Experts model (1600B total / 49B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run DeepSeek-V4-Pro on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.