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.
Want to run DeepSeek R2 faster — or step up to bigger models? Find your Mac →The LLMCheck index estimates DeepSeek R2 on M5 Max using our published methodology: Q4_K_M quantization, memory-bandwidth scaling, and cross-referenced third-party benchmarks where available. Figures are transparent estimates — own this config? Submit a real benchmark →
| Metric | Value |
|---|---|
| Tokens per second | 8 tok/s |
| Time to first token | 2.5s |
| Quantization | Q2_K |
| Minimum RAM | 128 GB |
| Recommended engine | MLX |
| Parameters | 671B |
| Benchmark date | 2026-05 |
Q2_K 671B MLX M5 Max
The recommended engine for DeepSeek R2 on M5 Max is MLX. Install with pip and pull the model:
Alternatively, you can use Ollama for a simpler setup:
To run DeepSeek R2 on M5 Max you need:
DeepSeek R2 needs about 128 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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See how DeepSeek R2 stacks up against other models on your specific Mac hardware.
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