MacBook Pro M4 Pro 64GBGünstige Wahl
64 GB Unified (+28)
C
Adds memory headroom for longer context windows and future model growth.20 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,599 MSRP
Can StableLM 2 12B run on MacBook Pro M4 Max 36GB?
YES — With Offload
C50Usable
Estimated from fit model
StableLM 2 12B needs ~25.6 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q5_K_M quantization, expect ~28 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
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, 27.9 tok/s, Runs with offload
25.6 GB required25.9 GB available
99% VRAM used
Fit status
Runs with offload
Decode
27.9 tok/s
TTFT
6942 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 M4 Max 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: 27.9 tok/s decode · 6.9s TTFT (warm) · 70 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 | 27.9 tok/s | 3786 ms | 4K |
| Coding | C | Runs with offload | 27.9 tok/s | 6942 ms | 4K |
| Agentic Coding | F | Too heavy | 16.8 tok/s | 16742 ms | 4K |
| Reasoning | C | Runs with offload | 27.9 tok/s | 8204 ms | 4K |
| RAG | F | Too heavy | 16.8 tok/s | 20928 ms | 4K |
Quantization options
How StableLM 2 12B (12B params) fits at each quantization level on MacBook Pro M4 Max 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 99Upgrade-Optionen
Hardware, die StableLM 2 12B gut ausführt
MacBook Pro M4 Max 48GBBestes Preis-Leistungs-Verhältnis
48 GB Unified (+12)546 GB/s (+136)
C
Adds memory headroom for longer context windows and future model growth.32.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
MacBook Pro M3 Max 48GBApple-Upgrade
48 GB Unified (+12)
C
Adds memory headroom for longer context windows and future model growth.25.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 Max 36GB run StableLM 2 12B?
Yes, MacBook Pro M4 Max 36GB can run StableLM 2 12B with a C grade (Runs with offload). Expected decode speed: 27.9 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 M4 Max 36GB?
On MacBook Pro M4 Max 36GB, StableLM 2 12B achieves approximately 27.9 tokens per second decode speed with a time-to-first-token of 6942ms using Q5_K_M quantization.
Can MacBook Pro M4 Max 36GB run StableLM 2 12B for coding?
For coding workloads, StableLM 2 12B on MacBook Pro M4 Max 36GB receives a C grade with 27.9 tok/s and 4K context.
What context window can StableLM 2 12B use on MacBook Pro M4 Max 36GB?
On MacBook Pro M4 Max 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 M4 Max 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 M4 Max 36GB as fast as VRAM for StableLM 2 12B?
Not always. MacBook Pro M4 Max 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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