Can Yi 1.5 9B run on MacBook Pro M3 24GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 9B needs ~10.4 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 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, 13.5 tok/s, Runs well

10.4 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

13.5 tok/s

TTFT

14373 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 MacBook Pro M3 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: 13.5 tok/s decode · 14.4s TTFT (warm) · 34 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	13.5 tok/s	7840 ms	4K
Coding	C	Runs well	13.5 tok/s	14373 ms	4K
Agentic Coding	C	Runs well	13.5 tok/s	20906 ms	4K
Reasoning	C	Runs well	13.5 tok/s	16986 ms	4K
RAG	C	Runs well	13.5 tok/s	26132 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on MacBook Pro M3 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

升级选项

能流畅运行 Yi 1.5 9B 的硬件

MacBook Pro M2 Max 32GB经济之选

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

Raises estimated decode speed by about 241%.46 tok/s 解码

Raises estimated decode speed by about 241%.

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

~$1,999 MSRP

MacBook Pro M1 Max 32GB最佳性价比

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

Raises estimated decode speed by about 223%.43.6 tok/s 解码

Raises estimated decode speed by about 223%.

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

~$2,499 MSRP

MacBook Pro M4 Max 36GBApple 升级

36 GB Unified (+12)410 GB/s (+310)

Raises estimated decode speed by about 313%.55.8 tok/s 解码

Raises estimated decode speed by about 313%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M3 24GB run Yi 1.5 9B?

Yes, MacBook Pro M3 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 13.5 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 MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Yi 1.5 9B achieves approximately 13.5 tokens per second decode speed with a time-to-first-token of 14373ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on MacBook Pro M3 24GB receives a C grade with 13.5 tok/s and 4K context.

What context window can Yi 1.5 9B use on MacBook Pro M3 24GB?

On MacBook Pro M3 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 MacBook Pro M3 24GB as fast as VRAM for Yi 1.5 9B?

Not always. MacBook Pro M3 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 MacBook Pro M3 24GB See all hardware for Yi 1.5 9B

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