MacBook Air M4 24GB经济之选
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.15.7 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Can Yi 1.5 9B run on MacBook Pro M4 16GB?
YES — Tight Fit
C53Usable
Estimated — low-sample bucket· few comparable runs
Yi 1.5 9B needs ~9.6 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~16 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 9.6 GB, 15.7 tok/s, Tight fit
9.6 GB required11.5 GB available
83% VRAM used
Fit status
Tight fit
Decode
15.7 tok/s
TTFT
12296 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 Pro M4 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: 15.7 tok/s decode · 12.3s TTFT (warm) · 39 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 | B | Runs well | 15.7 tok/s | 6707 ms | 4K |
| Coding | C | Tight fit | 15.7 tok/s | 12296 ms | 4K |
| Agentic Coding | C | Runs with offload | 15.7 tok/s | 17884 ms | 4K |
| Reasoning | C | Tight fit | 15.7 tok/s | 14531 ms | 4K |
| RAG | C | Runs with offload | 15.7 tok/s | 22355 ms | 4K |
Quantization options
How Yi 1.5 9B (9B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C55 |
Q3_K_S | 3 | 4.4 GB | Low | B56 |
NVFP4 | 4 | 5.0 GB | Medium | B57 |
Q4_K_M | 4 | 5.5 GB | Medium | B57 |
Q5_K_M | 5 | 6.5 GB | High | B57 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B56 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
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 M3 24GB最佳性价比
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.13.5 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Air M3 24GBApple 升级
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.13.5 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 3080 Ti 12GB最大飞跃912 GB/s (+792)
B
Raises estimated decode speed by about 588%.108 tok/s 解码
Raises estimated decode speed by about 588%.
~$1,199 MSRP
Frequently asked questions
Can MacBook Pro M4 16GB run Yi 1.5 9B?
Yes, MacBook Pro M4 16GB can run Yi 1.5 9B with a C grade (Tight fit). Expected decode speed: 15.7 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 Pro M4 16GB?
On MacBook Pro M4 16GB, Yi 1.5 9B achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12296ms using Q4_K_M quantization.
Can MacBook Pro M4 16GB run Yi 1.5 9B for coding?
For coding workloads, Yi 1.5 9B on MacBook Pro M4 16GB receives a C grade with 15.7 tok/s and 4K context.
What context window can Yi 1.5 9B use on MacBook Pro M4 16GB?
On MacBook Pro M4 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 Pro M4 16GB as fast as VRAM for Yi 1.5 9B?
Not always. MacBook Pro M4 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.
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