StableLM 2 12B needs ~23.5 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
12.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.8 tok/s
TTFT
28460 ms
Safe context
4K
Memory
23.5 GB / 11.5 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 23.5 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 | 8.8 tok/s | 12018 ms | 4K |
| Coding | F | Too heavy | 6.8 tok/s | 28460 ms | 4K |
| Agentic Coding | F | Too heavy | 6.8 tok/s | 41397 ms | 4K |
| Reasoning | F | Too heavy | 6.8 tok/s | 33635 ms | 4K |
| RAG | F | Too heavy | 6.8 tok/s | 51746 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C52 |
Q3_K_S | 3 | 5.9 GB | Low | C52 |
NVFP4 | 4 | 6.7 GB | Medium | C52 |
Q4_K_MBest for your GPU | 4 | 7.3 GB | Medium | C52 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
アップグレードオプション
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
〜$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.
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
RTX 5090 32GB最大の飛躍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.
〜$1,999 MSRP
No, StableLM 2 12B requires more memory than MacBook Pro M2 Pro 16GB provides.
StableLM 2 12B (12B parameters) requires approximately 23.5 GB of memory with Q5_K_M quantization.
The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, StableLM 2 12B achieves approximately 6.8 tokens per second decode speed with a time-to-first-token of 28460ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on MacBook Pro M2 Pro 16GB receives a F grade with 6.8 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, StableLM 2 12B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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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<iframe src="https://willitrunai.com/embed/stablelm-2-12b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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