AMD Instinct MI300X 192GBBudget pick
5300 GB/s (+4481)
C
Raises estimated decode speed by about 180%.168 tok/s decode
Raises estimated decode speed by about 180%.
~$15,000 MSRP
StableLM 2 12B needs ~49.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q5_K_M quantization, expect ~60 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
60.1 tok/s
TTFT
3220 ms
Safe context
4K
Memory
49.4 GB / 184.3 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 | 60.1 tok/s | 1756 ms | 4K |
| Coding | C | Runs well | 60.1 tok/s | 3220 ms | 4K |
| Agentic Coding | C | Runs well | 60.1 tok/s | 4683 ms | 4K |
| Reasoning | C | Runs well | 60.1 tok/s | 3805 ms | 4K |
| RAG | C | Runs well | 60.1 tok/s | 5854 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | D37 |
Q3_K_S | 3 | 5.9 GB | Low | D37 |
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
Raises estimated decode speed by about 180%.
~$15,000 MSRP
B100 192GBBest valueRaises estimated decode speed by about 180%.
~$35,000 MSRP
Yes, Mac Studio M3 Ultra 256GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 60.1 tok/s.
StableLM 2 12B (12B parameters) requires approximately 49.4 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 Mac Studio M3 Ultra 256GB, StableLM 2 12B achieves approximately 60.1 tokens per second decode speed with a time-to-first-token of 3220ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on Mac Studio M3 Ultra 256GB receives a C grade with 60.1 tok/s and 4K context.
On Mac Studio M3 Ultra 256GB, 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-ultra-256gb" 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 |
| D37 |
Q4_K_M | 4 | 7.3 GB | Medium | D37 |
Q5_K_M | 5 | 8.6 GB | High | D37 |
Q6_K | 6 | 9.8 GB | High | D37 |
Q8_0 | 8 | 12.8 GB | Very High | D37 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | D38 |
Not always. Mac Studio M3 Ultra 256GB 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.