RTX 5060 Ti 16GBコスパ重視16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.36.8 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Can gemma 3 12b it run on RTX 4070 Super 12GB?
YES — Tight Fit
C52Usable
Estimated from fit model
gemma 3 12b it needs ~10.8 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
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) — 10.8 GB, 55.7 tok/s, Tight fit
10.8 GB required12.0 GB available
90% VRAM used
Fit status
Tight fit
Decode
55.7 tok/s
TTFT
3478 ms
Safe context
29K
Memory
10.8 GB / 12.0 GB
Memory breakdown
Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsgemma 3 12b it on RTX 4070 Super 12GB
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: 55.7 tok/s decode · 3.5s TTFT (warm) · 139 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 | Tight fit | 55.7 tok/s | 1897 ms | 29K |
| Coding | C | Tight fit | 55.7 tok/s | 3478 ms | 29K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 40.1 tok/s | 7023 ms | 29K |
| Reasoning | C | Tight fit | 55.7 tok/s | 4110 ms | 29K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 40.1 tok/s | 8779 ms | 29K |
Quantization options
How gemma 3 12b it (12B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C52 |
Q3_K_S | 3 | 5.9 GB | Low | C52 |
NVFP4 | 4 | 6.7 GB | Medium | C52 |
Q4_K_M | 4 | 7.3 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 8.6 GB | High | C52 |
Q6_K | 6 | 9.8 GB | High | F0 |
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 startアップグレードオプション
gemma 3 12b itを快適に動かすハードウェア
RTX 5060 Ti 16GBコスパ重視RTX 4060 Ti 16GBコスパ最良16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレード16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.29.9 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$625 MSRP
Frequently asked questions
Can RTX 4070 Super 12GB run gemma 3 12b it?
Yes, RTX 4070 Super 12GB can run gemma 3 12b it with a C grade (Tight fit). Expected decode speed: 55.7 tok/s.
How much VRAM does gemma 3 12b it need?
gemma 3 12b it (12B parameters) requires approximately 10.8 GB of memory with Q4_K_M quantization.
What is the best quantization for gemma 3 12b it?
The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.
What speed will gemma 3 12b it run at on RTX 4070 Super 12GB?
On RTX 4070 Super 12GB, gemma 3 12b it achieves approximately 55.7 tokens per second decode speed with a time-to-first-token of 3478ms using Q4_K_M quantization.
Can RTX 4070 Super 12GB run gemma 3 12b it for coding?
For coding workloads, gemma 3 12b it on RTX 4070 Super 12GB receives a C grade with 55.7 tok/s and 29K context.
What context window can gemma 3 12b it use on RTX 4070 Super 12GB?
On RTX 4070 Super 12GB, gemma 3 12b it can safely use up to 29K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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