text model · DeepSeek-V3 · macOS

Can I run DeepSeek V3 on Apple M2 (16GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. DeepSeek V3 needs ~383.7 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

Needs ~383.7 GB Device usable ~10.5 GB

Needs ~383.7 GB even at Q4_K_M, but only ~10.5 GB is usable.

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

The gap is about 373.2 GB: DeepSeek V3 needs roughly 383.7 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. No single tracked device has enough memory; DeepSeek V3 needs a multi-GPU or high-memory rig.

Q4_K_M needed: ~383.7 GB
Usable on device: ~10.5 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~288 GB

Q3_K_M

~335 GB

Q4_K_M

~383.7 GB

Q5_K_M

~485.1 GB

Q6_K

~557.2 GB

Q8_0

~671.3 GB

FP16

~1349 GB

The line marks Apple M2 (16GB)'s ~10.5 GB budget; rungs past it are too large.

Model DeepSeek-V3

Parameters: 671B (MoE, 37B active)
Q4_K_M size: 376.65 GB
Q8_0 size: 664.3 GB
Context: 128k
Ollama tag: deepseek-v3:671b

Full DeepSeek V3 requirements →

Device macOS

Memory: 16 GB unified
Usable for weights: ~10.5 GB
Power draw: ~50 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M2 (16GB) →

What you can run instead

FAQ

Can Apple M2 (16GB) run DeepSeek V3?

No. DeepSeek V3 needs ~383.7 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

How much memory does DeepSeek V3 need?

Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~376.65 GB; with KV cache and runtime overhead, budget ~383.7 GB at a 4k context. It is a Mixture-of-Experts model (671B total / 37B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run DeepSeek V3 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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[![DeepSeek V3 on Apple M2 (16GB)](https://localmodel.run/badge/deepseek-v3/apple-m2-16gb.svg)](https://localmodel.run/can-i-run/deepseek-v3/apple-m2-16gb)

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.