MacBook Pro M4 32GBBudget pick
32 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.10.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
gemma 3 12b it needs ~11.4 GB VRAM. MacBook Pro M4 16GB has 11.5 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 with offload
Decode
10.4 tok/s
TTFT
18639 ms
Safe context
18K
Memory
11.4 GB / 11.5 GB
This setup is broadly balanced for this model.
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.
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.
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 | Tight fit | 10.4 tok/s | 10167 ms | 18K |
| Coding | C | Runs with offload | 10.4 tok/s | 18639 ms | 18K |
| Agentic Coding | D | Very compromised (needs ~0.7 GB host RAM) | 8.8 tok/s | 32082 ms | 18K |
| Reasoning | C | Runs with offload | 10.4 tok/s | 22028 ms | 18K |
| RAG | D | Very compromised (needs ~0.7 GB host RAM) | 8.8 tok/s | 40103 ms |
How gemma 3 12b it (12B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C52 |
Q3_K_S | 3 | 5.9 GB | Low | C52 |
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
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 5080 Laptop 16GBBiggest leapRaises estimated decode speed by about 747%.
Adds memory headroom for longer context windows and future model growth.
Yes, MacBook Pro M4 16GB can run gemma 3 12b it with a C grade (Runs with offload). Expected decode speed: 10.4 tok/s.
gemma 3 12b it (12B parameters) requires approximately 11.4 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 M4 16GB, 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 MacBook Pro M4 16GB receives a C grade with 10.4 tok/s and 18K context.
On MacBook Pro M4 16GB, gemma 3 12b it can safely use up to 18K 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-m4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 18K |
6.7 GB |
| Medium |
| C52 |
Q4_K_MBest for your GPU | 4 | 7.3 GB | Medium | C52 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M4 16GB 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.