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.

Capabilities:

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

Upgrade options

Hardware that runs gemma 3 12b it well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.36.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.30.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+1)672 GB/s (+56)

This setup is broadly balanced for this model.56.1 tok/s decode

~$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.

See all results for RTX 2080 Ti 11GB See all hardware for gemma 3 12b it

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