gemma 3 12b it needs ~12.3 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~73 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
73.4 tok/s
TTFT
2636 ms
Safe context
149K
Memory
12.3 GB / 24.0 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 | 73.4 tok/s | 1438 ms | 149K |
| Coding | C | Runs well | 73.4 tok/s | 2636 ms | 149K |
| Agentic Coding | C | Runs well | 73.4 tok/s | 3834 ms | 149K |
| Reasoning | C | Runs well | 73.4 tok/s | 3115 ms | 149K |
| RAG | C | Runs well | 73.4 tok/s | 4793 ms | 149K |
How gemma 3 12b it (12B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C45 |
Q3_K_S | 3 | 5.9 GB | Low | C46 |
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 A5000 24GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 73.4 tok/s.
gemma 3 12b it (12B parameters) requires approximately 12.3 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 A5000 24GB, gemma 3 12b it achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.
For coding workloads, gemma 3 12b it on RTX A5000 24GB receives a C grade with 73.4 tok/s and 149K context.
On RTX A5000 24GB, gemma 3 12b it can safely use up to 149K 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-a5000-24gb" 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 |
| C47 |
Q4_K_M | 4 | 7.3 GB | Medium | C47 |
Q5_K_M | 5 | 8.6 GB | High | C48 |
Q6_K | 6 | 9.8 GB | High | C49 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | C50 |
F16 | 16 | 24.6 GB | Maximum | F0 |