Mac mini M4 64GBBudget pick
64 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.8.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
gemma 3 27b it needs ~24.0 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~7 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
1.0 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.7 GB host RAM)
Decode
7.3 tok/s
TTFT
26532 ms
Safe context
11K
Memory
24.0 GB / 23.0 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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 | Runs with offload | 7.9 tok/s | 13379 ms | 11K |
| Coding | C | Runs with offload (needs ~0.7 GB host RAM) | 7.3 tok/s | 26532 ms | 11K |
| Agentic Coding | D | Very compromised (needs ~2.5 GB host RAM) | 6.1 tok/s | 45797 ms | 11K |
| Reasoning | C | Runs with offload (needs ~0.7 GB host RAM) | 7.3 tok/s | 31356 ms | 11K |
| RAG | D | Very compromised (needs ~2.5 GB host RAM) | 6.1 tok/s |
How gemma 3 27b it (27B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C50 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 189%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 358%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M1 Pro 32GB can run gemma 3 27b it with a C grade (Runs with offload (needs ~0.7 GB host RAM)). Expected decode speed: 7.3 tok/s.
gemma 3 27b it (27B parameters) requires approximately 24.0 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 M1 Pro 32GB, gemma 3 27b it achieves approximately 7.3 tokens per second decode speed with a time-to-first-token of 26532ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on MacBook Pro M1 Pro 32GB receives a C grade with 7.3 tok/s and 11K context.
On MacBook Pro M1 Pro 32GB, gemma 3 27b it can safely use up to 11K 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-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 57246 ms |
| 11K |
15.1 GB |
| Medium |
| C50 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | C50 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Pro M1 Pro 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.