gemma 3 12b it needs ~13.5 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~15 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
15.0 tok/s
TTFT
12942 ms
Safe context
157K
Memory
13.5 GB / 25.9 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 | 15.0 tok/s | 7059 ms | 157K |
| Coding | C | Runs well | 15.0 tok/s | 12942 ms | 157K |
| Agentic Coding | C | Runs well | 15.0 tok/s | 18825 ms | 157K |
| Reasoning | C | Runs well | 15.0 tok/s | 15295 ms | 157K |
| RAG | C | Runs well | 15.0 tok/s | 23531 ms | 157K |
How gemma 3 12b it (12B params) fits at each quantization level on MacBook Pro M3 Pro 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 |
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
Raises estimated decode speed by about 176%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Pro 36GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 15.0 tok/s.
gemma 3 12b it (12B parameters) requires approximately 13.5 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 M3 Pro 36GB, gemma 3 12b it achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12942ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on MacBook Pro M3 Pro 36GB receives a C grade with 15.0 tok/s and 157K context.
On MacBook Pro M3 Pro 36GB, gemma 3 12b it can safely use up to 157K tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--gemma-3-12b-it-gguf-on-m3-pro-36gb" 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 | C46 |
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 |
Not always. MacBook Pro M3 Pro 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.