Mac mini M4 64GBコスパ重視
64 GB Unified (+28)
C
Adds memory headroom for longer context windows and future model growth.8.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
Can StableLM 2 12B run on MacBook Pro M3 Pro 36GB?
YES — With Offload
C47Usable
Estimated from fit model
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.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 25.6 GB, 11.8 tok/s, Runs with offload
25.6 GB required25.9 GB available
99% VRAM used
Fit status
Runs with offload
Decode
11.8 tok/s
TTFT
16375 ms
Safe context
4K
Memory
25.6 GB / 25.9 GB
Memory breakdown
Weights8.6 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsStableLM 2 12B on MacBook Pro M3 Pro 36GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.8 tok/s decode · 16.4s TTFT (warm) · 30 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| 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 |
Quantization options
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 | 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 |
Get started
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 99アップグレードオプション
StableLM 2 12Bを快適に動かすハードウェア
MacBook Pro M4 Pro 64GBコスパ最良
64 GB Unified (+28)273 GB/s (+123)
C
Raises estimated decode speed by about 69%.20 tok/sデコード
Raises estimated decode speed by about 69%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
MacBook Pro M4 Max 48GBAppleアップグレード
48 GB Unified (+12)546 GB/s (+396)
C
Raises estimated decode speed by about 177%.32.7 tok/sデコード
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Frequently asked questions
Can MacBook Pro M3 Pro 36GB run StableLM 2 12B?
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.
How much VRAM does StableLM 2 12B need?
StableLM 2 12B (12B parameters) requires approximately 25.6 GB of memory with Q5_K_M quantization.
What is the best quantization for StableLM 2 12B?
The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.
What speed will StableLM 2 12B run at on MacBook Pro M3 Pro 36GB?
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.
Can MacBook Pro M3 Pro 36GB run StableLM 2 12B for coding?
For coding workloads, StableLM 2 12B on MacBook Pro M3 Pro 36GB receives a C grade with 11.8 tok/s and 4K context.
What context window can StableLM 2 12B use on MacBook Pro M3 Pro 36GB?
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.
What should I upgrade first if StableLM 2 12B feels slow on MacBook Pro M3 Pro 36GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for StableLM 2 12B?
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.
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