Mac mini M4 64GBBudget pick
64 GB Unified (+16)
B
Adds memory headroom for longer context windows and future model growth.6.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Gemma 2 27B needs ~33.8 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~17 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 with offload
Decode
16.8 tok/s
TTFT
11553 ms
Safe context
8K
Memory
33.8 GB / 34.6 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 16.8 tok/s | 6301 ms | 8K |
| Coding | B | Runs with offload | 16.8 tok/s | 11553 ms | 8K |
| Agentic Coding | F | Too heavy | 11.5 tok/s | 24389 ms | 8K |
| Reasoning | B | Runs with offload | 16.8 tok/s | 13653 ms | 8K |
| RAG | F | Too heavy | 11.5 tok/s | 30487 ms | 8K |
How Gemma 2 27B (27B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B65 |
Q3_K_S | 3 | 13.2 GB | Low | B66 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 Pro 48GB can run Gemma 2 27B with a B grade (Runs with offload). Expected decode speed: 16.8 tok/s.
Gemma 2 27B (27B parameters) requires approximately 33.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 48GB, Gemma 2 27B achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11553ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on MacBook Pro M4 Pro 48GB receives a B grade with 16.8 tok/s and 8K context.
On MacBook Pro M4 Pro 48GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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-2-27b-on-m4-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
15.1 GB |
| Medium |
| B67 |
Q4_K_M | 4 | 16.5 GB | Medium | B68 |
Q5_K_M | 5 | 19.4 GB | High | B69 |
Q6_KBest for your GPU | 6 | 22.1 GB | High | B68 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M4 Pro 48GB 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.