Can Yi 1.5 6B run on Mac mini M2 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

Yi 1.5 6B needs ~8.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~19 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) — 8.1 GB, 19.3 tok/s, Runs well

8.1 GB required17.3 GB available

47% VRAM used

Fit status

Runs well

Decode

19.3 tok/s

TTFT

10024 ms

Safe context

Memory

8.1 GB / 17.3 GB

Memory breakdown

Weights3.7 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsYi 1.5 6B 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: 19.3 tok/s decode · 10.0s TTFT (warm) · 48 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	19.3 tok/s	5468 ms	4K
Coding	C	Runs well	19.3 tok/s	10024 ms	4K
Agentic Coding	C	Runs well	19.3 tok/s	14580 ms	4K
Reasoning	C	Runs well	19.3 tok/s	11847 ms	4K
RAG	C	Runs well	19.3 tok/s	18226 ms	4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C46
Q3_K_S	3	2.9 GB	Low	C46
NVFP4	4	3.4 GB	Medium	C47
Q4_K_M	4	3.7 GB	Medium	C47
Q5_K_M	5	4.3 GB	High	C47
Q6_K	6	4.9 GB	High	C48
Q8_0	8	6.4 GB	Very High	C49
F16Best for your GPU	16	12.3 GB	Maximum	C50

Get started

Copy-paste commands to run Yi 1.5 6B on your machine.

Run

lms load Yi-1.5-6B-Chat && lms server start

Upgrade-Optionen

Hardware, die Yi 1.5 6B gut ausführt

MacBook Pro M2 Max 32GBGünstige Wahl

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

Raises estimated decode speed by about 257%.68.9 tok/s Dekodierung

Raises estimated decode speed by about 257%.

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

ca. $1,999 MSRP

MacBook Pro M2 Pro 32GBBestes Preis-Leistungs-Verhältnis

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

Raises estimated decode speed by about 116%.41.6 tok/s Dekodierung

Raises estimated decode speed by about 116%.

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

ca. $1,999 MSRP

Mac Studio M2 Ultra 64GBApple-Upgrade

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

Raises estimated decode speed by about 335%.84 tok/s Dekodierung

Raises estimated decode speed by about 335%.

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

ca. $3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Yi 1.5 6B?

Yes, Mac mini M2 24GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 19.3 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B?

The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Yi 1.5 6B achieves approximately 19.3 tokens per second decode speed with a time-to-first-token of 10024ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on Mac mini M2 24GB receives a C grade with 19.3 tok/s and 4K context.

What context window can Yi 1.5 6B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Yi 1.5 6B 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 6B?

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 6B

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