gemma 3 12b it needs ~11.5 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 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
42.8 tok/s
TTFT
4519 ms
Safe context
67K
Memory
11.5 GB / 16.0 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 | 42.8 tok/s | 2465 ms | 67K |
| Coding | C | Runs well | 42.8 tok/s | 4519 ms | 67K |
| Agentic Coding | C | Runs well | 42.8 tok/s | 6573 ms | 67K |
| Reasoning | C | Runs well | 42.8 tok/s | 5341 ms | 67K |
| RAG | C | Runs well | 42.8 tok/s | 8216 ms | 67K |
How gemma 3 12b it (12B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C49 |
Q3_K_S | 3 | 5.9 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run gemma 3 12b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server startYes, RTX A4000 16GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 42.8 tok/s.
gemma 3 12b it (12B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.
On RTX A4000 16GB, gemma 3 12b it achieves approximately 42.8 tokens per second decode speed with a time-to-first-token of 4519ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on RTX A4000 16GB receives a C grade with 42.8 tok/s and 67K context.
On RTX A4000 16GB, gemma 3 12b it can safely use up to 67K 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-maziyarpanahi--gemma-3-12b-it-gguf-on-a4000-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
6.7 GB |
| Medium |
| C51 |
Q4_K_M | 4 | 7.3 GB | Medium | C51 |
Q5_K_M | 5 | 8.6 GB | High | C52 |
Q6_KBest for your GPU | 6 | 9.8 GB | High | C51 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |