~$2,499 MSRP
gemma 3 27b it needs ~24.0 GB VRAM. RTX 5000 Ada 32GB has 32.0 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
28.0 tok/s
TTFT
6920 ms
Safe context
56K
Memory
24.0 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.0 tok/s | 3774 ms | 56K |
| Coding | C | Runs well | 28.0 tok/s | 6920 ms | 56K |
| Agentic Coding | C | Tight fit | 28.0 tok/s | 10065 ms | 56K |
| Reasoning | C | Runs well | 28.0 tok/s | 8178 ms | 56K |
| RAG | C | Tight fit | 28.0 tok/s | 12581 ms | 56K |
How gemma 3 27b it (27B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C47 |
Q3_K_S | 3 | 13.2 GB | Low | C48 |
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
~$2,499 MSRP
Raises estimated decode speed by about 183%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Yes, RTX 5000 Ada 32GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 28.0 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 RTX 5000 Ada 32GB, gemma 3 27b it achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6920ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on RTX 5000 Ada 32GB receives a C grade with 28.0 tok/s and 56K context.
On RTX 5000 Ada 32GB, gemma 3 27b it can safely use up to 56K 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-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C49 |
Q4_K_M | 4 | 16.5 GB | Medium | C50 |
Q5_K_M | 5 | 19.4 GB | High | C49 |
Q6_KBest for your GPU | 6 | 22.1 GB | High | C49 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |