baichuan inc Baichuan M2 32B needs ~34.5 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
11.9 tok/s
TTFT
16289 ms
Safe context
164K
Memory
34.5 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 | 11.9 tok/s | 8885 ms | 164K |
| Coding | C | Runs well | 11.9 tok/s | 16289 ms | 164K |
| Agentic Coding | C | Runs well | 11.9 tok/s | 23693 ms | 164K |
| Reasoning | C | Runs well | 11.9 tok/s | 19251 ms | 164K |
| RAG | C | Runs well | 11.9 tok/s | 29617 ms | 164K |
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C41 |
Q3_K_S | 3 | 15.7 GB | Low | C42 |
NVFP4 | 4 |
Copy-paste commands to run baichuan inc Baichuan M2 32B on your machine.
Run
lms load hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 159%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 11.9 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 34.5 GB of memory with Q4_K_M quantization.
The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, baichuan inc Baichuan M2 32B achieves approximately 11.9 tokens per second decode speed with a time-to-first-token of 16289ms using Q4_K_M quantization.
For coding workloads, baichuan inc Baichuan M2 32B on MacBook Pro M2 Max 96GB receives a C grade with 11.9 tok/s and 164K context.
On MacBook Pro M2 Max 96GB, baichuan inc Baichuan M2 32B can safely use up to 164K 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-bartowski--baichuan-inc-baichuan-m2-32b-gguf-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:
17.9 GB |
| Medium |
| C42 |
Q4_K_M | 4 | 19.5 GB | Medium | C42 |
Q5_K_M | 5 | 23.0 GB | High | C43 |
Q6_K | 6 | 26.2 GB | High | C44 |
Q8_0Best for your GPU | 8 | 34.2 GB | Very High | C46 |
F16 | 16 | 65.6 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.