Can Yi 1.5 9B run on Mac mini M2 24GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 9B needs ~10.4 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.4 GB, 12.9 tok/s, Runs well

10.4 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

12.9 tok/s

TTFT

15036 ms

Safe context

Memory

10.4 GB / 17.3 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on Mac mini M2 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 12.9 tok/s decode · 15.0s TTFT (warm) · 32 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	12.9 tok/s	8202 ms	4K
Coding	C	Runs well	12.9 tok/s	15036 ms	4K
Agentic Coding	C	Runs well	12.9 tok/s	21871 ms	4K
Reasoning	C	Runs well	12.9 tok/s	17770 ms	4K
RAG	C	Runs well	12.9 tok/s	27338 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C51
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C54
Q6_K	6	7.4 GB	High	C55
Q8_0Best for your GPU	8	9.6 GB	Very High	B56
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Yi 1.5 9B on your machine.

Run

lms load Yi-1.5-9B-Chat && lms server start

Upgrade options

Hardware that runs Yi 1.5 9B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 257%.46 tok/s decode

Raises estimated decode speed by about 257%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 115%.27.7 tok/s decode

Raises estimated decode speed by about 115%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+40)800 GB/s (+700)

Raises estimated decode speed by about 612%.91.9 tok/s decode

Raises estimated decode speed by about 612%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Yi 1.5 9B?

Yes, Mac mini M2 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 12.9 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B?

The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 9B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Yi 1.5 9B achieves approximately 12.9 tokens per second decode speed with a time-to-first-token of 15036ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on Mac mini M2 24GB receives a C grade with 12.9 tok/s and 4K context.

What context window can Yi 1.5 9B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Yi 1.5 9B?

Not always. Mac mini M2 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M2 24GB See all hardware for Yi 1.5 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

12.9 tok/s

8202 ms

Coding

Runs well

12.9 tok/s

15036 ms

Agentic Coding

Runs well

12.9 tok/s

21871 ms

Reasoning

Runs well

12.9 tok/s

17770 ms

RAG

Runs well

12.9 tok/s

27338 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

C51

Q3_K_S

4.4 GB

Low

C52

NVFP4

5.0 GB

Medium

C52

Q4_K_M

5.5 GB

Medium

C53

Q5_K_M

6.5 GB

High

C54

Q6_K

7.4 GB

High

C55

Q8_0Best for your GPU

9.6 GB

Very High

B56

F16

18.5 GB

Maximum

Frequently asked questions

Can Mac mini M2 24GB run Yi 1.5 9B?

Yes, Mac mini M2 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 12.9 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B?

The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 9B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Yi 1.5 9B achieves approximately 12.9 tokens per second decode speed with a time-to-first-token of 15036ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on Mac mini M2 24GB receives a C grade with 12.9 tok/s and 4K context.

What context window can Yi 1.5 9B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Yi 1.5 9B?