Will It Run AI

Can Yi 1.5 9B run on MacBook Air M2 16GB?

YES — Tight Fit

C52Usable
Estimated from fit model

Yi 1.5 9B needs ~9.6 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 9.6 GB, 12.9 tok/s, Tight fit
9.6 GB required11.5 GB available
83% VRAM used

Fit status

Tight fit

Decode

12.9 tok/s

TTFT

15036 ms

Safe context

4K

Memory

9.6 GB / 11.5 GB

Memory breakdown

Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.7 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on MacBook Air M2 16GB
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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well12.9 tok/s8202 ms4K
CodingCTight fit12.9 tok/s15036 ms4K
Agentic CodingCRuns with offload12.9 tok/s21871 ms4K
ReasoningCTight fit12.9 tok/s17770 ms4K
RAGCRuns with offload12.9 tok/s27338 ms4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC55
Q3_K_S
3
4.4 GB
LowB56
NVFP4
4
5.0 GB
MediumB57
Q4_K_M
4
5.5 GB
MediumB57
Q5_K_M
5
6.5 GB
HighB57
Q6_KBest for your GPU
6
7.4 GB
HighB56
Q8_0
8
9.6 GB
Very HighF0
F16
16
18.5 GB
MaximumF0

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-Optionen

Hardware, die Yi 1.5 9B gut ausführt

MacBook Air M4 24GBGünstige Wahl
24 GB Unified (+8)120 GB/s (+20)
C
Adds memory headroom for longer context windows and future model growth.15.7 tok/s Dekodierung

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

ca. $1,099 MSRP

MacBook Pro M3 24GBBestes Preis-Leistungs-Verhältnis
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.13.5 tok/s Dekodierung

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

ca. $1,099 MSRP

MacBook Air M3 24GBApple-Upgrade
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.13.5 tok/s Dekodierung

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

ca. $1,099 MSRP

NVIDIARTX 3080 Ti 12GBGrößter Sprung
912 GB/s (+812)
B
Raises estimated decode speed by about 737%.108 tok/s Dekodierung

Raises estimated decode speed by about 737%.

ca. $1,199 MSRP

Frequently asked questions

Can MacBook Air M2 16GB run Yi 1.5 9B?

Yes, MacBook Air M2 16GB can run Yi 1.5 9B with a C grade (Tight fit). Expected decode speed: 12.9 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 9.6 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 Air M2 16GB?

On MacBook Air M2 16GB, 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 MacBook Air M2 16GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on MacBook Air M2 16GB receives a C grade with 12.9 tok/s and 4K context.

What context window can Yi 1.5 9B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, 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 Air M2 16GB as fast as VRAM for Yi 1.5 9B?

Not always. MacBook Air M2 16GB 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 Air M2 16GBSee all hardware for Yi 1.5 9B
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