Intel Arc Pro B60 24GBBest value456 GB/s (+256)
C
Raises estimated decode speed by about 76%.33.6 tok/s decode
Raises estimated decode speed by about 76%.
~$599 MSRP
gemma 3 12b it needs ~13.1 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~19 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
19.1 tok/s
TTFT
10123 ms
Safe context
129K
Memory
13.1 GB / 23.0 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 | 19.1 tok/s | 5521 ms | 129K |
| Coding | C | Runs well | 19.1 tok/s | 10123 ms | 129K |
| Agentic Coding | C | Runs well | 19.1 tok/s | 14724 ms | 129K |
| Reasoning | C | Runs well | 19.1 tok/s | 11963 ms | 129K |
| RAG | C | Runs well | 19.1 tok/s | 18405 ms | 129K |
How gemma 3 12b it (12B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C46 |
Q3_K_S | 3 | 5.9 GB | Low | C46 |
NVFP4 | 4 | 6.7 GB | Medium | C47 |
Q4_K_M | 4 | 7.3 GB | Medium | C47 |
Q5_K_M | 5 | 8.6 GB | High | C48 |
Q6_K | 6 | 9.8 GB | High | C49 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | C50 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Copy-paste commands to run gemma 3 12b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server startUpgrade options
Intel Arc Pro B60 24GBBest valueRaises estimated decode speed by about 76%.
~$599 MSRP
Raises estimated decode speed by about 85%.
~$2,499 MSRP
Yes, MacBook Pro M2 Pro 32GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 19.1 tok/s.
gemma 3 12b it (12B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, gemma 3 12b it achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10123ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on MacBook Pro M2 Pro 32GB receives a C grade with 19.1 tok/s and 129K context.
On MacBook Pro M2 Pro 32GB, gemma 3 12b it can safely use up to 129K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M2 Pro 32GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--gemma-3-12b-it-gguf-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: