Gemma 4 31B needs ~41.2 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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
Tight fit
Decode
10.2 tok/s
TTFT
18984 ms
Safe context
21K
Memory
41.2 GB / 46.1 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 | S | Runs well | 9.7 tok/s | 10873 ms | 21K |
| Coding | A | Tight fit | 9.7 tok/s | 19933 ms | 21K |
| Agentic Coding | F | Too heavy | 7.3 tok/s | 38539 ms | 21K |
| Reasoning | A | Tight fit | 9.7 tok/s | 23557 ms | 21K |
| RAG | F | Too heavy | 7.3 tok/s | 48173 ms | 21K |
How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.0 GB | Low | A81 |
Q3_K_S | 3 | 15.0 GB | Low | A82 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 4 31B on your machine.
Run
ollama run gemma4:31bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 35B | S | 33.5 tok/s | ||
Yes, MacBook Pro M3 Max 64GB can run Gemma 4 31B with a A grade (Tight fit). Expected decode speed: 9.7 tok/s.
Gemma 4 31B (30.700000762939453B parameters) requires approximately 41.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, Gemma 4 31B achieves approximately 9.7 tokens per second decode speed with a time-to-first-token of 19933ms using Q4_K_M quantization.
For coding workloads, Gemma 4 31B on MacBook Pro M3 Max 64GB receives a A grade with 9.7 tok/s and 21K context.
On MacBook Pro M3 Max 64GB, Gemma 4 31B can safely use up to 21K tokens of context. The model's official context limit is 256K, 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-4-31b-on-m3-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
17.2 GB |
| Medium |
| A82 |
Q4_K_M | 4 | 18.7 GB | Medium | A83 |
Q5_K_M | 5 | 22.1 GB | High | A84 |
Q6_K | 6 | 25.2 GB | High | S85 |
Q8_0Best for your GPU | 8 | 32.8 GB | Very High | A85 |
F16 | 16 | 62.9 GB | Maximum | F0 |
| 35B |
| S |
| 36.5 tok/s |
| 32B | S | 13.4 tok/s |
Not always. MacBook Pro M3 Max 64GB 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.