Question 1

Can I run DeepSeek R1 Distill Qwen 1.5B on a Apple M2 Pro?

Accepted Answer

Yes — Apple M2 Pro runs DeepSeek R1 Distill Qwen 1.5B fully on GPU at the Q8_0 quantization. Full GPU inference — 32 GB VRAM meets the 4 GB recommendation.

Question 2

What quantization of DeepSeek R1 Distill Qwen 1.5B should I use on a Apple M2 Pro?

Accepted Answer

For 32 GB VRAM on the Apple M2 Pro, the Q8_0 variant is the best fit. Estimated ~119 tokens/sec on the Q8_0 quantization.

Question 3

How fast does DeepSeek R1 Distill Qwen 1.5B run on Apple M2 Pro?

Accepted Answer

Roughly 119 tokens/sec for Q8_0. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if Apple M2 Pro is not enough for DeepSeek R1 Distill Qwen 1.5B?

Accepted Answer

Try a smaller quantization variant or run on CPU + RAM with the trade-off of slower generation.

Quantization	File Size	Min VRAM	Rec VRAM	Context	Verdict	Estimated tok/s
Q4_K_M	1 GB	2 GB	4 GB	8K / 128K	Full GPU	~160
Q8_0Best fit	1.6 GB	2.5 GB	4 GB	8K / 128K	Full GPU	~119

Can I Run DeepSeek R1 Distill Qwen 1.5B on Apple M2 Pro?

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Every DeepSeek R1 Distill Qwen 1.5B quantization on Apple M2 Pro