How fast is DeepSeek R1 on an Mac?

Around 2 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 100 GB/s. Because this is a mixture-of-experts model only 37.44B of its 684.49B parameters are read per token, which is why it is faster than its size suggests.

How much memory does DeepSeek R1 need?

390.5 GiB for the weights at Q4_K_M, plus 0.5 GiB for an 8K-token KV cache and about 20.3 GiB of runtime overhead — 411.3 GiB in total.

Home › DeepSeek R1 › M2 24GB

Can an M2 24GB Mac run DeepSeek R1?

M2 · 24 GB unified memory · 100 GB/s · MacBook Air 15" (M2), Mac mini (M2)

No — not at Q4_K_M.

DeepSeek R1 needs about 411.3 GiB but an M2 24GB Mac has only ~20.4 GiB available for a model — you are 390.9 GiB short. macOS will swap to SSD and generation will crawl.

Weights

390.5 GiB

684.49B params @ Q4_K_M

KV cache

0.5 GiB

8K context, FP16

Overhead

20.3 GiB

runtime + activations

Total needed

411.3 GiB

of ~20.4 GiB usable

Est. speed

2 tok/s

100 GB/s bandwidth

Max context

n/a

at Q4_K_M

Where the memory goes

Three things occupy memory when a model is loaded: the weights themselves, the KV cache that holds the conversation, and the runtime's own working buffers. Only the first is fixed — the KV cache grows with every token in your context.

Quantisation	Weights	Total	Tok/s	Fits?
FP16	1275.0 GiB	1340.0 GiB	1	No
Q8_0	677.3 GiB	712.5 GiB	1	No
Q6_K	525.9 GiB	553.6 GiB	2	No
Q5_K_M	454.2 GiB	478.3 GiB	2	No
Q4_K_M	390.5 GiB	411.3 GiB	2	No
Q3_K_M	310.8 GiB	327.6 GiB	3	No
Q2_K	239.1 GiB	252.3 GiB	4	No

The default. Best quality-per-gigabyte for most people.

How long a context fits

The KV cache is often what breaks a setup that looked fine at load time. Quantising it to 8-bit roughly halves its footprint with little measurable quality cost.

Context	KV cache	Total	FP16 KV	Q8 KV
2K	0.1 GiB	410.7 GiB	No	No
4K	0.3 GiB	410.9 GiB	No	No
8K	0.5 GiB	411.3 GiB	No	No
16K	1.1 GiB	412.2 GiB	No	No
32K	2.2 GiB	413.8 GiB	No	No
128K	8.6 GiB	423.9 GiB	No	No

What to run instead on an M2 24GB Mac

These are the largest models that fit comfortably on this machine at Q4_K_M with an 8K context.

Model	Size	Total	Tok/s
Mistral Small 3 24B	23.6B	16.2 GiB	6	Yes, comfortably
gpt-oss-20b	20.9B	13.7 GiB	23	Yes, comfortably
DeepSeek-R1-Distill-Qwen-14B	14.8B	11.2 GiB	9	Yes, comfortably
Qwen2.5 14B	14.8B	11.2 GiB	9	Yes, comfortably
Qwen2.5-Coder 14B	14.8B	11.2 GiB	9	Yes, comfortably
Qwen3 14B	14.8B	10.9 GiB	9	Yes, comfortably

Macs that run DeepSeek R1 comfortably

The smallest configuration that handles it is the M3 Ultra 512GB (Mac Studio (M3 Ultra)).

Mac	Memory	Bandwidth	Tok/s
M3 Ultra 512GB	512 GB	819 GB/s	18

Common questions

Can an M2 24GB Mac run DeepSeek R1?: No. At Q4_K_M it needs about 411.3 GiB, and an M2 24GB Mac has roughly 20.4 GiB available for a model after macOS takes its share.
How fast is DeepSeek R1 on an Mac?: Around 2 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 100 GB/s. Because this is a mixture-of-experts model only 37.44B of its 684.49B parameters are read per token, which is why it is faster than its size suggests.
How much memory does DeepSeek R1 need?: 390.5 GiB for the weights at Q4_K_M, plus 0.5 GiB for an 8K-token KV cache and about 20.3 GiB of runtime overhead — 411.3 GiB in total.