Gemma 3 4B needs ~9.2 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~56 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
123K
Memory
9.2 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 | B | Runs well | 55.9 tok/s | 1888 ms | 123K |
| Coding | A | Runs well | 55.9 tok/s | 3461 ms | 123K |
| Agentic Coding | A | Runs well | 55.9 tok/s | 5034 ms | 123K |
| Reasoning | A | Runs well | 55.9 tok/s | 4090 ms | 123K |
| RAG | A | Runs well | 55.9 tok/s | 6292 ms | 123K |
How Gemma 3 4B (4B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B65 |
Q3_K_S | 3 | 2.0 GB | Low | B65 |
NVFP4 | 4 | 2.2 GB | Medium | B66 |
Q4_K_M | 4 | 2.4 GB | Medium | B66 |
Q5_K_M | 5 | 2.9 GB | High | B66 |
Q6_K | 6 | 3.3 GB | High | B66 |
Q8_0 | 8 | 4.3 GB | Very High | B67 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B69 |
Copy-paste commands to run Gemma 3 4B on your machine.
Run
ollama run gemma3:4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 17.4 tok/s | |
| Qwen 3.5 27B | 27B | A | 7.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 8.6 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 18.3 tok/s | |
| Qwen 3.5 9B | 9B | S | 25.5 tok/s |
Yes, MacBook Pro M1 Pro 32GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 55.9 tok/s.
Gemma 3 4B (4B parameters) requires approximately 9.2 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 32GB, Gemma 3 4B achieves approximately 55.9 tokens per second decode speed with a time-to-first-token of 3461ms using Q4_K_M quantization.
For coding workloads, Gemma 3 4B on MacBook Pro M1 Pro 32GB receives a A grade with 55.9 tok/s and 123K context.
On MacBook Pro M1 Pro 32GB, Gemma 3 4B can safely use up to 123K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 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/gemma-3-4b-on-m1-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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