Yi 1.5 6B Chat needs ~12.2 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~57 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
52.8 tok/s
TTFT
3664 ms
Safe context
788K
Memory
12.2 GB / 46.1 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 | 52.8 tok/s | 1998 ms | 788K |
| Coding | C | Runs well | 57.4 tok/s | 3371 ms | 788K |
| Agentic Coding | C | Runs well | 52.8 tok/s | 5329 ms | 788K |
| Reasoning | C | Runs well | 52.8 tok/s | 4330 ms | 788K |
| RAG | C | Runs well | 52.8 tok/s | 6662 ms | 788K |
How Yi 1.5 6B Chat (6B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C42 |
Q3_K_S | 3 | 2.9 GB | Low | C42 |
NVFP4 | 4 |
Copy-paste commands to run Yi 1.5 6B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-1-5-6b-chat-gguf && lms server startUpgrade options
Raises estimated decode speed by about 59%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 59%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 59%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 64GB can run Yi 1.5 6B Chat with a C grade (Runs well). Expected decode speed: 57.4 tok/s.
Yi 1.5 6B Chat (6B parameters) requires approximately 12.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 6B Chat is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 64GB, Yi 1.5 6B Chat achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3371ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 6B Chat on MacBook Pro M4 Pro 64GB receives a C grade with 57.4 tok/s and 788K context.
On MacBook Pro M4 Pro 64GB, Yi 1.5 6B Chat can safely use up to 788K tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--yi-1-5-6b-chat-gguf-on-m4-pro-64gb" 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 |
| C42 |
Q4_K_M | 4 | 3.7 GB | Medium | C42 |
Q5_K_M | 5 | 4.3 GB | High | C42 |
Q6_K | 6 | 4.9 GB | High | C42 |
Q8_0 | 8 | 6.4 GB | Very High | C42 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C44 |
Not always. MacBook Pro M4 Pro 64GB 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.