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 |
Inference speed
Estimated decode speed (tokens/sec) for gemma 3 12b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~164 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 164.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 104.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 94.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 89.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 87.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 76.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 63.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 60.1 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 54.2 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 41.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 41.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 32.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 32.5 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 30.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 25.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 11.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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>
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