20 GB VRAM (+4)640 GB/s (+352)
C
Raises estimated decode speed by about 126%.68.2 tok/s decode
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
~$2,000 MSRP
Can gemma 3 12b it run on RTX 4060 Ti 16GB?
YES — Runs Great
C53Usable
○Estimated from fit model
gemma 3 12b it needs ~11.2 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 11.2 GB, 30.2 tok/s, Runs well
11.2 GB required16.0 GB available
70% VRAM used
Fit status
Runs well
Decode
30.2 tok/s
TTFT
6420 ms
Safe context
70K
Memory
11.2 GB / 16.0 GB
Memory breakdown
Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsgemma 3 12b it on RTX 4060 Ti 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 30.2 tok/s decode · 6.4s TTFT (warm) · 75 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 30.2 tok/s | 3502 ms | 70K |
| Coding | C | Runs well | 30.2 tok/s | 6420 ms | 70K |
| Agentic Coding | C | Runs well | 30.2 tok/s | 9338 ms | 70K |
| Reasoning | C | Runs well | 30.2 tok/s | 7587 ms | 70K |
| RAG | C | Runs well | 30.2 tok/s | 11672 ms | 70K |
Quantization options
How gemma 3 12b it (12B params) fits at each quantization level on RTX 4060 Ti 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 | 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 |
Get started
Copy-paste commands to run gemma 3 12b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server startUpgrade options