RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+798)
C
Raises estimated decode speed by about 206%.100 tok/s decode
Raises estimated decode speed by about 206%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
StableLM 2 12B needs ~26.9 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q5_K_M quantization, expect ~33 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 well
Decode
32.7 tok/s
TTFT
5923 ms
Safe context
4K
Memory
26.9 GB / 34.6 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 32.7 tok/s | 3231 ms | 4K |
| Coding | C | Runs well | 32.7 tok/s | 5923 ms | 4K |
| Agentic Coding | D | Very compromised (needs ~1 GB host RAM) | 26.8 tok/s | 10503 ms | 4K |
| Reasoning | C | Runs well | 37.1 tok/s | 6160 ms | 4K |
| RAG | D | Very compromised (needs ~1 GB host RAM) | 26.8 tok/s | 13128 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C43 |
Q3_K_S | 3 | 5.9 GB | Low | C43 |
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
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 206%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA A100 40GBBest valueRaises estimated decode speed by about 254%.
~$10,000 MSRP
Yes, MacBook Pro M4 Max 48GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 32.7 tok/s.
StableLM 2 12B (12B parameters) requires approximately 26.9 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 M4 Max 48GB, StableLM 2 12B achieves approximately 32.7 tokens per second decode speed with a time-to-first-token of 5923ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on MacBook Pro M4 Max 48GB receives a C grade with 32.7 tok/s and 4K context.
On MacBook Pro M4 Max 48GB, 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-m4-max-48gb" 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 |
| C44 |
Q4_K_M | 4 | 7.3 GB | Medium | C44 |
Q5_K_M | 5 | 8.6 GB | High | C44 |
Q6_K | 6 | 9.8 GB | High | C45 |
Q8_0 | 8 | 12.8 GB | Very High | C46 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | C48 |
Not always. MacBook Pro M4 Max 48GB 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.