Gemma 3 4B needs ~7.4 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~53 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
55.9 tok/s
TTFT
3461 ms
Safe context
47K
Memory
7.4 GB / 11.5 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 | A | Runs well | 53.3 tok/s | 1982 ms | 47K |
| Coding | A | Runs well | 53.3 tok/s | 3634 ms | 47K |
| Agentic Coding | A | Tight fit | 53.3 tok/s | 5285 ms | 47K |
| Reasoning | A | Runs well | 53.3 tok/s | 4294 ms | 47K |
| RAG | A | Tight fit | 53.3 tok/s | 6607 ms | 47K |
How Gemma 3 4B (4B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B69 |
Q3_K_S | 3 | 2.0 GB | Low | B70 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 3 4B on your machine.
Run
ollama run gemma3:4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 25.5 tok/s | |
| Qwen 3 8B | 8B | S | 28.6 tok/s |
Yes, MacBook Pro M1 Pro 16GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 53.3 tok/s.
Gemma 3 4B (4B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 16GB, Gemma 3 4B achieves approximately 53.3 tokens per second decode speed with a time-to-first-token of 3634ms using Q4_K_M quantization.
For coding workloads, Gemma 3 4B on MacBook Pro M1 Pro 16GB receives a A grade with 53.3 tok/s and 47K context.
On MacBook Pro M1 Pro 16GB, Gemma 3 4B can safely use up to 47K tokens of context. The model's official context limit is 128K, 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/gemma-3-4b-on-m1-pro-16gb" 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 |
| B70 |
Q4_K_M | 4 | 2.4 GB | Medium | A70 |
Q5_K_M | 5 | 2.9 GB | High | A71 |
Q6_K | 6 | 3.3 GB | High | A71 |
Q8_0 | 8 | 4.3 GB | Very High | A73 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A73 |
 Nemotron Nano 8B | 8B | S | 28.6 tok/s |
 InternVL2 8B | 8B | A | 28.6 tok/s |
 Ministral 3 8B | 8B | A | 28.6 tok/s |
Not always. MacBook Pro M1 Pro 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.