How fast is Qwen2.5 1.5B on an Mac?

Around 58 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 68 GB/s.

How much memory does Qwen2.5 1.5B need?

0.9 GiB for the weights at Q4_K_M, plus 0.2 GiB for an 8K-token KV cache and about 0.8 GiB of runtime overhead — 1.9 GiB in total.

Home › Qwen2.5 1.5B › M1 8GB

Can an M1 8GB Mac run Qwen2.5 1.5B?

M1 · 8 GB unified memory · 68 GB/s · MacBook Air (M1), MacBook Pro 13" (M1)

Yes — Qwen2.5 1.5B runs on an M1 8GB Mac.

At Q4_K_M it needs about 1.9 GiB, leaving 3.1 GiB spare out of the ~5.0 GiB macOS will let you use. Expect roughly 58 tokens/sec — faster than you can read.

Weights

0.9 GiB

1.54B params @ Q4_K_M

KV cache

0.2 GiB

8K context, FP16

Overhead

0.8 GiB

runtime + activations

Total needed

1.9 GiB

of ~5.0 GiB usable

Est. speed

58 tok/s

68 GB/s bandwidth

Max context

32K tokens

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	2.9 GiB	4.0 GiB	18	Yes, comfortably
Q8_0	1.5 GiB	2.6 GiB	33	Yes, comfortably
Q6_K	1.2 GiB	2.3 GiB	43	Yes, comfortably
Q5_K_M	1.0 GiB	2.1 GiB	50	Yes, comfortably
Q4_K_M	0.9 GiB	1.9 GiB	58	Yes, comfortably
Q3_K_M	0.7 GiB	1.8 GiB	72	Yes, comfortably
Q2_K	0.5 GiB	1.6 GiB	94	Yes, comfortably

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	1.6 GiB	Yes, comfortably	Yes, comfortably
4K	0.1 GiB	1.7 GiB	Yes, comfortably	Yes, comfortably
8K	0.2 GiB	1.9 GiB	Yes, comfortably	Yes, comfortably
16K	0.4 GiB	2.5 GiB	Yes, comfortably	Yes, comfortably
32K	0.9 GiB	3.5 GiB	Yes, comfortably	Yes, comfortably

How to run it

Ollama is the shortest path. It picks a quantisation automatically — usually Q4_K_M, which is what the numbers above assume.

brew install ollama
ollama serve &
ollama run qwen2.5:1-5b

For MLX — Apple's own array framework, typically a little faster than llama.cpp on Apple Silicon:

pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen2-5-1-5B-4bit --prompt "Hello"

Macs that run Qwen2.5 1.5B comfortably

The smallest configuration that handles it is the M1 8GB (MacBook Air (M1)).

Mac	Memory	Bandwidth	Tok/s
M1 8GB	8 GB	68 GB/s	58
M1 16GB	16 GB	68 GB/s	58
M1 Pro 16GB	16 GB	200 GB/s	170
M2 16GB	16 GB	100 GB/s	85
M4 16GB	16 GB	120 GB/s	102
M3 Pro 18GB	18 GB	150 GB/s	127
M2 24GB	24 GB	100 GB/s	85
M4 24GB	24 GB	120 GB/s	102
M4 Pro 24GB	24 GB	273 GB/s	232
M1 Max 32GB	32 GB	400 GB/s	339
M2 Max 32GB	32 GB	400 GB/s	339
M4 32GB	32 GB	120 GB/s	102

Common questions

Can an M1 8GB Mac run Qwen2.5 1.5B?: Yes. At Q4_K_M it needs about 1.9 GiB of the roughly 5.0 GiB available, generating around 58 tokens per second.
How fast is Qwen2.5 1.5B on an Mac?: Around 58 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 68 GB/s.
How much memory does Qwen2.5 1.5B need?: 0.9 GiB for the weights at Q4_K_M, plus 0.2 GiB for an 8K-token KV cache and about 0.8 GiB of runtime overhead — 1.9 GiB in total.