Can Gemma 3 4B run on MacBook Pro M3 Pro 18GB?
YES — Runs Great
Gemma 3 4B needs ~7.7 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~47 tok/s.
Operating mode
Choose the run profile you care about
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
47.1 tok/s
TTFT
4109 ms
Safe context
57K
Memory
7.7 GB / 13.0 GB
Memory breakdown
See how fast it feels
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 47.1 tok/s | 2241 ms | 57K |
| Coding | A | Runs well | 47.1 tok/s | 4109 ms | 57K |
| Agentic Coding | A | Runs well | 47.1 tok/s | 5976 ms | 57K |
| Reasoning | A | Runs well | 47.1 tok/s | 4856 ms | 57K |
| RAG | A | Runs well | 47.1 tok/s | 7470 ms | 57K |
Quantization options
How Gemma 3 4B (4B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B68 |
Q3_K_S | 3 | 2.0 GB | Low | B69 |
NVFP4 | 4 | 2.2 GB | Medium | B69 |
Q4_K_M | 4 | 2.4 GB | Medium | B69 |
Q5_K_M | 5 | 2.9 GB | High | B70 |
Q6_K | 6 | 3.3 GB | High | A70 |
Q8_0 | 8 | 4.3 GB | Very High | A71 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A73 |
Get started
Copy-paste commands to run Gemma 3 4B on your machine.
Run
ollama run gemma3:4bYour hardware
More models your MacBook Pro M3 Pro 18GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 👁 Alibaba Qwen 3.5 9B | 9B | S | 21.4 tok/s | |
| 👁 Alibaba Qwen 3 14B | 14B | A | 12 tok/s | |
| 👁 Alibaba Qwen 3 8B | 8B | S | 24.1 tok/s | |
| 👁 Microsoft Phi-4-reasoning-plus 14B | 14.7B | A | 10.3 tok/s | |
| 👁 NVIDIA Nemotron Nano 8B | 8B | S | 24.1 tok/s |
