MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+290)
B
Raises estimated decode speed by about 255%.40.8 tok/s decode
Raises estimated decode speed by about 255%.
~$2,499 MSRP
Gemma 2 9B needs ~15.0 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~12 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
11.5 tok/s
TTFT
16803 ms
Safe context
8K
Memory
15.0 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 | 11.5 tok/s | 9166 ms | 8K |
| Coding | B | Runs well | 11.5 tok/s | 16803 ms | 8K |
| Agentic Coding | B | Tight fit | 11.5 tok/s | 24441 ms | 8K |
| Reasoning | B | Runs well | 11.5 tok/s | 19859 ms | 8K |
| RAG | B | Tight fit | 11.5 tok/s | 30552 ms | 8K |
How Gemma 2 9B (9B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B59 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 | 5.0 GB | Medium | B60 |
Q4_K_M | 4 | 5.5 GB | Medium | B60 |
Q5_K_M | 5 | 6.5 GB | High | B61 |
Q6_K | 6 | 7.4 GB | High | B62 |
Q8_0 | 8 | 9.6 GB | Very High | B63 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B64 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Raises estimated decode speed by about 255%.
~$2,499 MSRP
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 485%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 32GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 11.5 tok/s.
Gemma 2 9B (9B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 32GB, Gemma 2 9B achieves approximately 11.5 tokens per second decode speed with a time-to-first-token of 16803ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on Mac mini M4 32GB receives a B grade with 11.5 tok/s and 8K context.
On Mac mini M4 32GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-9b-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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