Gemma 2 27B needs ~35.5 GB VRAM. Mac mini M4 64GB has 46.1 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
Fit status
Runs well
Decode
6.9 tok/s
TTFT
28108 ms
Safe context
8K
Memory
35.5 GB / 46.1 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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 6.9 tok/s | 15331 ms | 8K |
| Coding | B | Runs well | 6.9 tok/s | 28108 ms | 8K |
| Agentic Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 6.7 tok/s | 42289 ms | 8K |
| Reasoning | B | Runs well | 6.9 tok/s | 33218 ms | 8K |
| RAG | B | Runs with offload (needs ~0.2 GB host RAM) | 6.7 tok/s | 52861 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for Gemma 2 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 58.2 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 26.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 26.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 26.6 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 22.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 21.3 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 20.9 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 17.9 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 16.8 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 12.6 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 11.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 7.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 3.6 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.3 | 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 2 27B (27B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B63 |
Q3_K_S | 3 | 13.2 GB | Low | B64 |
NVFP4 | 4 | 15.1 GB | Medium | B64 |
Q4_K_M | 4 | 16.5 GB | Medium | B65 |
Q5_K_M | 5 | 19.4 GB | High | B66 |
Q6_K | 6 | 22.1 GB | High | B67 |
Q8_0Best for your GPU | 8 | 28.9 GB | Very High | B68 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bOpções de upgrade
Raises estimated decode speed by about 286%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 290%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 6.9 tok/s.
Gemma 2 27B (27B parameters) requires approximately 35.5 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 Mac mini M4 64GB, Gemma 2 27B achieves approximately 6.9 tokens per second decode speed with a time-to-first-token of 28108ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on Mac mini M4 64GB receives a B grade with 6.9 tok/s and 8K context.
On Mac mini M4 64GB, 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.
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. Mac mini M4 64GB 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-2-27b-on-m4-mini-64gb" 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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