Mac mini M4 64GBBudget pick
64 GB Unified (+28)
C
Adds memory headroom for longer context windows and future model growth.8.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
StableLM 2 12B needs ~25.6 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q5_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 with offload
Decode
11.8 tok/s
TTFT
16375 ms
Safe context
4K
Memory
25.6 GB / 25.9 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 11.8 tok/s | 8932 ms | 4K |
| Coding | C | Runs with offload | 11.8 tok/s | 16375 ms | 4K |
| Agentic Coding | F | Too heavy | 7.1 tok/s | 39493 ms | 4K |
| Reasoning | C | Runs with offload | 11.8 tok/s | 19352 ms | 4K |
| RAG | F | Too heavy | 7.1 tok/s | 49366 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C45 |
Q3_K_S | 3 | 5.9 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run StableLM 2 12B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "stabilityai/stablelm-2-12b-chat" \
--hf-file "stablelm-2-12b-chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 69%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run StableLM 2 12B with a C grade (Runs with offload). Expected decode speed: 11.8 tok/s.
StableLM 2 12B (12B parameters) requires approximately 25.6 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 M3 Pro 36GB, StableLM 2 12B achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16375ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on MacBook Pro M3 Pro 36GB receives a C grade with 11.8 tok/s and 4K context.
On MacBook Pro M3 Pro 36GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/stablelm-2-12b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
6.7 GB |
| Medium |
| C46 |
Q4_K_M | 4 | 7.3 GB | Medium | C46 |
Q5_K_M | 5 | 8.6 GB | High | C47 |
Q6_K | 6 | 9.8 GB | High | C47 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | C49 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M3 Pro 36GB 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.