MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+40)
C
Adds memory headroom for longer context windows and future model growth.20 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
StableLM 2 12B needs ~24.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q5_K_M quantization, expect ~20 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
0.1 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
20.1 tok/s
TTFT
9641 ms
Safe context
4K
Memory
24.1 GB / 24.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
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 | 26.6 tok/s | 3972 ms | 4K |
| Coding | C | Runs with offload | 20.1 tok/s | 9641 ms | 4K |
| Agentic Coding | F | Too heavy | 8.7 tok/s | 32311 ms | 4K |
| Reasoning | C | Runs with offload | 20.1 tok/s | 11394 ms | 4K |
| RAG | F | Too heavy | 8.7 tok/s | 40389 ms | 4K |
How StableLM 2 12B (12B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C45 |
Q3_K_S | 3 | 5.9 GB | Low | C46 |
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.
~$1,599 MSRP
Raises estimated decode speed by about 103%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Yes, Intel Arc Pro B60 24GB can run StableLM 2 12B with a C grade (Runs with offload). Expected decode speed: 20.1 tok/s.
StableLM 2 12B (12B parameters) requires approximately 24.1 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 Intel Arc Pro B60 24GB, StableLM 2 12B achieves approximately 20.1 tokens per second decode speed with a time-to-first-token of 9641ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on Intel Arc Pro B60 24GB receives a C grade with 20.1 tok/s and 4K context.
On Intel Arc Pro B60 24GB, 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-arc-pro-b60-24gb" 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 | C47 |
Q5_K_M | 5 | 8.6 GB | High | C47 |
Q6_K | 6 | 9.8 GB | High | C48 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | C50 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.