Raises estimated decode speed by about 38%.
~$1,999 MSRP
Yi 1.5 9B needs ~11.3 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 tok/s.
Operating mode
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
Fit status
Runs well
Decode
15.7 tok/s
TTFT
12296 ms
Safe context
4K
Memory
11.3 GB / 23.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 15.7 tok/s | 6707 ms | 4K |
| Coding | C | Runs well | 15.7 tok/s | 12296 ms | 4K |
| Agentic Coding | C | Runs well | 15.7 tok/s | 17884 ms | 4K |
| Reasoning | C | Runs well | 15.7 tok/s | 14538 ms | 4K |
| RAG | C | Runs well | 15.7 tok/s | 22355 ms | 4K |
How Yi 1.5 9B (9B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C49 |
Q3_K_S | 3 | 4.4 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startUpgrade options
Raises estimated decode speed by about 38%.
~$1,999 MSRP
Raises estimated decode speed by about 255%.
~$2,499 MSRP
Raises estimated decode speed by about 373%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 32GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 15.7 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 11.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, 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.
For coding workloads, Yi 1.5 9B on MacBook Pro M4 32GB receives a C grade with 15.7 tok/s and 4K context.
On MacBook Pro M4 32GB, 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.
Not always. MacBook Pro M4 32GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 5.5 GB | Medium | C50 |
Q5_K_M | 5 | 6.5 GB | High | C51 |
Q6_K | 6 | 7.4 GB | High | C52 |
Q8_0 | 8 | 9.6 GB | Very High | C53 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C54 |