Baichuan M2 32B Q4 K M needs ~25.9 GB but MacBook Pro M2 Pro 16GB only has 11.5 GB. Try a smaller quantization or lighter model.
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
14.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
59985 ms
Safe context
4K
Memory
25.9 GB / 11.5 GB
Offload
60%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 25.9 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.2 tok/s | 32719 ms | 4K |
| Coding | F | Too heavy | 3.2 tok/s | 59985 ms | 4K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 87251 ms | 4K |
| Reasoning | F | Too heavy | 3.2 tok/s | 70892 ms | 4K |
| RAG | F | Too heavy | 3.2 tok/s | 109064 ms | 4K |
How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | F0 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
NVFP4 | 4 | 17.9 GB | Medium | F0 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 91%.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 91%.
~$1,099 MSRP
NVIDIA A100 40GB最大飞跃Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$10,000 MSRP
No, Baichuan M2 32B Q4 K M requires more memory than MacBook Pro M2 Pro 16GB provides.
Baichuan M2 32B Q4 K M (32B parameters) requires approximately 25.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Baichuan M2 32B Q4 K M achieves approximately 3.2 tokens per second decode speed with a time-to-first-token of 59985ms using Q4_K_M quantization.
For coding workloads, Baichuan M2 32B Q4 K M on MacBook Pro M2 Pro 16GB receives a F grade with 3.2 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, Baichuan M2 32B Q4 K M can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M2 Pro 16GB 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.
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Preview:
