gemma 3 27b it needs ~24.4 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~16 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
26.7 tok/s
TTFT
7264 ms
Safe context
24K
Memory
24.4 GB / 25.9 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 | C | Tight fit | 15.7 tok/s | 6733 ms | 24K |
| Coding | C | Tight fit | 15.7 tok/s | 12345 ms | 24K |
| Agentic Coding | D | Runs with offload | 14.0 tok/s | 20082 ms | 24K |
| Reasoning | C | Tight fit | 15.7 tok/s | 14589 ms | 24K |
| RAG | D | Runs with offload | 14.0 tok/s | 25102 ms | 24K |
How gemma 3 27b it (27B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C49 |
Q3_K_S | 3 | 13.2 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-unsloth--gemma-3-27b-it-gguf && lms server startUpgrade options
Raises estimated decode speed by about 25%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,999 MSRP
Yes, MacBook Pro M4 Max 36GB can run gemma 3 27b it with a C grade (Tight fit). Expected decode speed: 15.7 tok/s.
gemma 3 27b it (27B parameters) requires approximately 24.4 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 36GB, gemma 3 27b it achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12345ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on MacBook Pro M4 Max 36GB receives a C grade with 15.7 tok/s and 24K context.
On MacBook Pro M4 Max 36GB, gemma 3 27b it can safely use up to 24K tokens of context. The model's official context limit is —, 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/hf-unsloth--gemma-3-27b-it-gguf-on-m4-max-36gb" 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 |
| C50 |
Q4_K_M | 4 | 16.5 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 19.4 GB | High | C49 |
Q6_K | 6 | 22.1 GB | High | F0 |
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 Max 36GB 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.