Yi 34B Chat needs ~35.7 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~12 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
12.1 tok/s
TTFT
15941 ms
Safe context
162K
Memory
35.7 GB / 69.1 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 | 12.1 tok/s | 8695 ms | 162K |
| Coding | C | Runs well | 12.1 tok/s | 15941 ms | 162K |
| Agentic Coding | C | Runs well | 11.2 tok/s | 25174 ms | 162K |
| Reasoning | C | Runs well | 12.1 tok/s | 18839 ms | 162K |
| RAG | C | Runs well | 12.1 tok/s | 28983 ms | 162K |
How Yi 34B Chat (34B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | C43 |
Q3_K_S | 3 | 16.7 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run Yi 34B Chat on your machine.
Run
lms load Yi-34B-Chat && lms server startUpgrade options
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 90%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 160%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 12.1 tok/s.
Yi 34B Chat (34B parameters) requires approximately 35.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, Yi 34B Chat achieves approximately 12.1 tokens per second decode speed with a time-to-first-token of 15941ms using Q4_K_M quantization.
For coding workloads, Yi 34B Chat on MacBook Pro M2 Max 96GB receives a C grade with 12.1 tok/s and 162K context.
On MacBook Pro M2 Max 96GB, Yi 34B Chat can safely use up to 162K tokens of context. The model's official context limit is 200K, 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-34b-chat-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
19.0 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 20.7 GB | Medium | C45 |
Q5_K_M | 5 | 24.5 GB | High | C45 |
Q6_K | 6 | 27.9 GB | High | C46 |
Q8_0Best for your GPU | 8 | 36.4 GB | Very High | C49 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Not always. MacBook Pro M2 Max 96GB 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.