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

Around 695 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 819 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 › M3 Ultra 256GB

Can an M3 Ultra 256GB Mac run Qwen2.5 1.5B?

M3 Ultra · 256 GB unified memory · 819 GB/s · Mac Studio (M3 Ultra)

Yes — Qwen2.5 1.5B runs on an M3 Ultra 256GB Mac.

At Q4_K_M it needs about 1.9 GiB, leaving 238.1 GiB spare out of the ~240.0 GiB macOS will let you use. Expect roughly 695 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 ~240.0 GiB usable

Est. speed

695 tok/s

819 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	213	Yes, comfortably
Q8_0	1.5 GiB	2.6 GiB	400	Yes, comfortably
Q6_K	1.2 GiB	2.3 GiB	516	Yes, comfortably
Q5_K_M	1.0 GiB	2.1 GiB	597	Yes, comfortably
Q4_K_M	0.9 GiB	1.9 GiB	695	Yes, comfortably
Q3_K_M	0.7 GiB	1.8 GiB	873	Yes, comfortably
Q2_K	0.5 GiB	1.6 GiB	1135	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 M3 Ultra 256GB Mac run Qwen2.5 1.5B?: Yes. At Q4_K_M it needs about 1.9 GiB of the roughly 240.0 GiB available, generating around 695 tokens per second.
How fast is Qwen2.5 1.5B on an Mac?: Around 695 tokens per second. Token generation on Apple Silicon is limited by memory bandwidth, and this machine has 819 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.