RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.91.4 tok/s decode
Raises estimated decode speed by about 224%.
~$9,999 MSRP
gemma 3 27b it needs ~34.4 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~28 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
28.2 tok/s
TTFT
6872 ms
Safe context
308K
Memory
34.4 GB / 92.2 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 | C | Runs well | 28.2 tok/s | 3748 ms | 308K |
| Coding | C | Runs well | 28.2 tok/s | 6872 ms | 308K |
| Agentic Coding | C | Runs well | 28.2 tok/s | 9996 ms | 308K |
| Reasoning | C | Runs well | 28.2 tok/s | 8121 ms | 308K |
| RAG | C | Runs well | 28.2 tok/s | 12494 ms | 308K |
How gemma 3 27b it (27B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | D40 |
Q3_K_S | 3 | 13.2 GB | Low | C40 |
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
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 224%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 189%.
~$9,999 MSRP
Yes, Mac Studio M2 Ultra 128GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 28.2 tok/s.
gemma 3 27b it (27B parameters) requires approximately 34.4 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 Mac Studio M2 Ultra 128GB, gemma 3 27b it achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6872ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on Mac Studio M2 Ultra 128GB receives a C grade with 28.2 tok/s and 308K context.
On Mac Studio M2 Ultra 128GB, gemma 3 27b it can safely use up to 308K 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-m2-ultra-128gb" 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 |
| C40 |
Q4_K_M | 4 | 16.5 GB | Medium | C41 |
Q5_K_M | 5 | 19.4 GB | High | C41 |
Q6_K | 6 | 22.1 GB | High | C41 |
Q8_0 | 8 | 28.9 GB | Very High | C43 |
F16Best for your GPU | 16 | 55.4 GB | Maximum | C48 |
Not always. Mac Studio M2 Ultra 128GB 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.