RTX 5060 Ti 16GBコスパ重視16 GB VRAM (+5)
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 2080 Ti 11GB?
YES — With Offload
C52Usable
Estimated from fit model
gemma 3 12b it needs ~10.7 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 10.7 GB, 54.7 tok/s, Runs with offload
10.7 GB required11.0 GB available
97% VRAM used
Fit status
Runs with offload
Decode
54.7 tok/s
TTFT
3539 ms
Safe context
19K
Memory
10.7 GB / 11.0 GB
Memory breakdown
Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsgemma 3 12b it on RTX 2080 Ti 11GB
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: 54.7 tok/s decode · 3.5s TTFT (warm) · 137 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 54.7 tok/s | 1931 ms | 19K |
| Coding | C | Runs with offload | 54.7 tok/s | 3539 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.7 GB host RAM) | 32.2 tok/s | 8753 ms | 19K |
| Reasoning | C | Runs with offload | 54.7 tok/s | 4183 ms | 19K |
| RAG | C | Very compromised (needs ~0.7 GB host RAM) | 32.2 tok/s | 10941 ms | 19K |
Quantization options
How gemma 3 12b it (12B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C53 |
Q3_K_S | 3 | 5.9 GB | Low | C52 |
NVFP4 | 4 | 6.7 GB | Medium | C52 |
Q4_K_MBest for your GPU | 4 | 7.3 GB | Medium | C52 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
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 (+5)
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 5070 12GBNVIDIAアップグレード12 GB VRAM (+1)672 GB/s (+56)
C
This setup is broadly balanced for this model.56.1 tok/sデコード
〜$549 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run gemma 3 12b it?
Yes, RTX 2080 Ti 11GB can run gemma 3 12b it with a C grade (Runs with offload). Expected decode speed: 54.7 tok/s.
How much VRAM does gemma 3 12b it need?
gemma 3 12b it (12B parameters) requires approximately 10.7 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 2080 Ti 11GB?
On RTX 2080 Ti 11GB, gemma 3 12b it achieves approximately 54.7 tokens per second decode speed with a time-to-first-token of 3539ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run gemma 3 12b it for coding?
For coding workloads, gemma 3 12b it on RTX 2080 Ti 11GB receives a C grade with 54.7 tok/s and 19K context.
What context window can gemma 3 12b it use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, gemma 3 12b it can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if gemma 3 12b it feels slow on RTX 2080 Ti 11GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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