gemma 3 12b it needs ~16.5 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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
10.4 tok/s
TTFT
18639 ms
Safe context
352K
Memory
16.5 GB / 46.1 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 | 10.4 tok/s | 10167 ms | 352K |
| Coding | C | Runs well | 10.4 tok/s | 18639 ms | 352K |
| Agentic Coding | C | Runs well | 10.4 tok/s | 27111 ms | 352K |
| Reasoning | C | Runs well | 10.4 tok/s | 22028 ms | 352K |
| RAG | C | Runs well | 10.4 tok/s | 33889 ms | 352K |
How gemma 3 12b it (12B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C42 |
Q3_K_S | 3 | 5.9 GB | Low | C42 |
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 298%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 215%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 632%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 10.4 tok/s.
gemma 3 12b it (12B parameters) requires approximately 16.5 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 Mac mini M4 64GB, gemma 3 12b it achieves approximately 10.4 tokens per second decode speed with a time-to-first-token of 18639ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on Mac mini M4 64GB receives a C grade with 10.4 tok/s and 352K context.
On Mac mini M4 64GB, gemma 3 12b it can safely use up to 352K 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-m4-mini-64gb" 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 |
| C42 |
Q4_K_M | 4 | 7.3 GB | Medium | C42 |
Q5_K_M | 5 | 8.6 GB | High | C43 |
Q6_K | 6 | 9.8 GB | High | C43 |
Q8_0 | 8 | 12.8 GB | Very High | C44 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | C48 |
Not always. Mac mini M4 64GB 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.