MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+4)150 GB/s (+30)
C
Raises estimated decode speed by about 38%.44.9 tok/s decode
Raises estimated decode speed by about 38%.
~$1,999 MSRP
gemma 3 4b it needs ~7.3 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~33 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
32.6 tok/s
TTFT
5943 ms
Safe context
554K
Memory
7.3 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 | 32.6 tok/s | 3242 ms | 554K |
| Coding | C | Runs well | 32.6 tok/s | 5943 ms | 554K |
| Agentic Coding | C | Runs well | 32.6 tok/s | 8644 ms | 554K |
| Reasoning | C | Runs well | 32.6 tok/s | 7023 ms | 554K |
| RAG | C | Runs well | 32.6 tok/s | 10805 ms | 554K |
How gemma 3 4b it (4B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C44 |
Q3_K_S | 3 | 2.0 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run gemma 3 4b it on your machine.
Run
lms load hf-lmstudio-community--gemma-3-4b-it-gguf && lms server startUpgrade options
Raises estimated decode speed by about 38%.
~$1,999 MSRP
Raises estimated decode speed by about 72%.
~$2,499 MSRP
Raises estimated decode speed by about 72%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 32GB can run gemma 3 4b it with a C grade (Runs well). Expected decode speed: 32.6 tok/s.
gemma 3 4b it (4B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, gemma 3 4b it achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5943ms using Q4_K_M quantization.
For coding workloads, gemma 3 4b it on MacBook Pro M4 32GB receives a C grade with 32.6 tok/s and 554K context.
On MacBook Pro M4 32GB, gemma 3 4b it can safely use up to 554K 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-lmstudio-community--gemma-3-4b-it-gguf-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.2 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 2.4 GB | Medium | C45 |
Q5_K_M | 5 | 2.9 GB | High | C45 |
Q6_K | 6 | 3.3 GB | High | C45 |
Q8_0 | 8 | 4.3 GB | Very High | C46 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | C48 |
Not always. MacBook Pro M4 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.