Gemma 2 27B needs ~35.5 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~21 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
21.3 tok/s
TTFT
9107 ms
Safe context
8K
Memory
35.5 GB / 46.1 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 | A | Runs well | 21.3 tok/s | 4967 ms | 8K |
| Coding | A | Runs well | 21.3 tok/s | 9107 ms | 8K |
| Agentic Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 20.6 tok/s | 13702 ms | 8K |
| Reasoning | A | Runs well | 21.3 tok/s | 10763 ms | 8K |
| RAG | B | Runs with offload (needs ~0.2 GB host RAM) | 20.6 tok/s | 17127 ms |
How Gemma 2 27B (27B params) fits at each quantization level on Mac Studio M1 Ultra 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 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 66.5 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 55.9 tok/s |
Yes, Mac Studio M1 Ultra 64GB can run Gemma 2 27B with a A grade (Runs well). Expected decode speed: 21.3 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 Studio M1 Ultra 64GB, Gemma 2 27B achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9107ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on Mac Studio M1 Ultra 64GB receives a A grade with 21.3 tok/s and 8K context.
On Mac Studio M1 Ultra 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-m1-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
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 |
| Qwen3-VL 30B A3B Instruct | 30B | S | 68.8 tok/s |
| Qwen 3.5 35B A3B | 35B | S | 60.8 tok/s |
| Qwen 3 32B | 32B | S | 24.5 tok/s |
Not always. Mac Studio M1 Ultra 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.