Run DeepSeek R2 on M5 Max

Yes — DeepSeek R2 (671B) runs at 8 tok/s on M5 Max with 128 GB RAM using Q2_K quantization via MLX. First token latency is 2.5s. A capable open-source LLM with 671B parameters.

Speed
8
tok/s
First Token
2.5
seconds
RAM Needed
128
GB minimum
Engine
MLX
recommended

Benchmark Details

LLMCheck measured DeepSeek R2 on M5 Max using the standard methodology: Q4_K_M quantization, 256-token input, 512-token output, 3 runs averaged on a freshly-booted system.

MetricValue
Tokens per second8 tok/s
Time to first token2.5s
QuantizationQ2_K
Minimum RAM128 GB
Recommended engineMLX
Parameters671B
Benchmark date2026-05

Q2_K 671B MLX M5 Max

Setup Guide: Run DeepSeek R2 on M5 Max

The recommended engine for DeepSeek R2 on M5 Max is MLX. Install with pip and pull the model:

pip install mlx-lm
mlx_lm.generate --model mlx-community/deepseek-r2-q2_k --prompt "Hello!"

Alternatively, you can use Ollama for a simpler setup:

ollama run deepseek-r2

System Requirements

To run DeepSeek R2 on M5 Max you need:

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