Gemma 4 31B needs ~37.7 GB but Mac mini M4 32GB only has 23.0 GB. Try a smaller quantization or lighter model.
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
14.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.5 tok/s
TTFT
55297 ms
Safe context
4K
Memory
37.7 GB / 23.0 GB
Offload
40%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 37.7 GB, but this setup only exposes 23.0 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.5 tok/s | 23559 ms | 4K |
| Coding | F | Too heavy | 3.5 tok/s | 55297 ms | 4K |
| Agentic Coding | F | Too heavy | 3.0 tok/s | 94695 ms | 4K |
| Reasoning | F | Too heavy | 3.5 tok/s | 65351 ms | 4K |
| RAG | F | Too heavy | 3.0 tok/s | 118368 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Gemma 4 31B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~26 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 25.5 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 25.5 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 23.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 19.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 18.7 | Fits |
 RTX 5090 32GB | 32 GB | Q4_K_M | 15.0 | Heavy offload |
| RTX 4090 24GB | 24 GB | Q4_K_M | 13.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 13.0 | Heavy offload |
| RTX 3090 24GB | 24 GB | Q4_K_M | 11.1 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 10.2 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 9.3 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 7.8 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 5.1 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 3.2 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.0 GB | Low | S87 |
Q3_K_S | 3 | 15.0 GB | Low | S87 |
NVFP4Best for your GPU | 4 | 17.2 GB | Medium | S86 |
Q4_K_M | 4 | 18.7 GB | Medium | F0 |
Q5_K_M | 5 | 22.1 GB | High | F0 |
Q6_K | 6 | 25.2 GB | High | F0 |
Q8_0 | 8 | 32.8 GB | Very High | F0 |
F16 | 16 | 62.9 GB | Maximum | F0 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,599 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$3,999 MSRP
No, Gemma 4 31B requires more memory than Mac mini M4 32GB provides.
Gemma 4 31B (30.700000762939453B parameters) requires approximately 37.7 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 Mac mini M4 32GB, Gemma 4 31B achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 55297ms using Q4_K_M quantization.
For coding workloads, Gemma 4 31B on Mac mini M4 32GB receives a F grade with 3.5 tok/s and 4K context.
On Mac mini M4 32GB, Gemma 4 31B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. Mac mini 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.
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<iframe src="https://willitrunai.com/embed/gemma-4-31b-on-m4-mini-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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