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text model · DeepSeek-V2 · macOS

Can I run DeepSeek-V2-Lite on Apple M4 Max (64GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated

Yes. DeepSeek-V2-Lite runs on Apple M4 Max (64GB) at Q4_K_M (~12.2 GB of ~48 GB usable).

Needs ~12.2 GB Device usable ~48 GB

Runs at Q4_K_M using ~12.2 GB of ~48 GB usable. You have room for FP16 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Max (64GB) leaves ~35.8 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~12.2 GB
Usable on device
~48 GB
Device memory
64 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~48 GB usable
Q2_K
~8.5 GB
Q3_K_M
~9.6 GB
Q4_K_M
~12.2 GB
Q5_K_M
~13.2 GB
Q6_K
~14.9 GB
Q8_0
~18.6 GB
FP16
~33.8 GB
The line marks Apple M4 Max (64GB)'s ~48 GB budget; rungs past it are too large.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run deepseek-v2:16b
llama.cpp
$ llama-cli -hf mradermacher/DeepSeek-V2-Lite-GGUF:Q4_K_M
LM Studio
$ lms get mradermacher/DeepSeek-V2-Lite-GGUF
Model DeepSeek-V2
Parameters
16B (MoE, 2.4B active)
Q4_K_M size
10.4 GB
Q8_0 size
16.8 GB
Context
32k
Ollama tag
deepseek-v2:16b
Full DeepSeek-V2-Lite requirements →
Device macOS
Memory
64 GB unified
Usable for weights
~48 GB
Power draw
~145 W
Best runtime
MLX direct / Ollama (MLX backend)
Best models for Apple M4 Max (64GB) →

You could also run

Run DeepSeek-V2-Lite on other hardware

FAQ

Can Apple M4 Max (64GB) run DeepSeek-V2-Lite?

Yes. DeepSeek-V2-Lite runs on Apple M4 Max (64GB) at Q4_K_M (~12.2 GB of ~48 GB usable).

How much memory does DeepSeek-V2-Lite need?

Apple M4 Max (64GB) has room to spare. At Q4_K_M the weights are ~10.4 GB; with KV cache and runtime overhead, budget ~12.2 GB at a 4k context. It is a Mixture-of-Experts model (16B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run DeepSeek-V2-Lite 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.