Yi 1.5 6B needs ~9.4 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~30 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
32.5 tok/s
TTFT
5950 ms
Safe context
4K
Memory
9.4 GB / 25.9 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 | 32.5 tok/s | 3246 ms | 4K |
| Coding | C | Runs well | 29.9 tok/s | 6471 ms | 4K |
| Agentic Coding | C | Runs well | 32.5 tok/s | 8655 ms | 4K |
| Reasoning | C | Runs well | 32.5 tok/s | 7032 ms | 4K |
| RAG | C | Runs well | 32.5 tok/s | 10819 ms | 4K |
How Yi 1.5 6B (6B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run Yi 1.5 6B on your machine.
Run
lms load Yi-1.5-6B-Chat && lms server startUpgrade options
Raises estimated decode speed by about 158%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 29.9 tok/s.
Yi 1.5 6B (6B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Yi 1.5 6B achieves approximately 29.9 tokens per second decode speed with a time-to-first-token of 6471ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 6B on MacBook Pro M3 Pro 36GB receives a C grade with 29.9 tok/s and 4K context.
On MacBook Pro M3 Pro 36GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-6b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.4 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 3.7 GB | Medium | C44 |
Q5_K_M | 5 | 4.3 GB | High | C44 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C45 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C49 |
Not always. MacBook Pro M3 Pro 36GB 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.