Can gemma 3 12b it run on RTX 3080 12GB?

Q: What should I upgrade first if gemma 3 12b it feels slow on RTX 3080 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

C54Usable

Estimated from fit model

gemma 3 12b it needs ~11.1 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: 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) — 11.1 GB, 94.7 tok/s, Tight fit

11.1 GB required12.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

94.7 tok/s

TTFT

2045 ms

Safe context

26K

Memory

11.1 GB / 12.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on RTX 3080 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: 94.7 tok/s decode · 2.0s TTFT (warm) · 237 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.

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	94.7 tok/s	1115 ms	26K
Coding	C	Tight fit	94.7 tok/s	2045 ms	26K
Agentic Coding	C	Runs with offload (needs ~0.3 GB host RAM)	64.8 tok/s	4345 ms	26K
Reasoning	C	Tight fit	94.7 tok/s	2416 ms	26K
RAG	C	Runs with offload (needs ~0.3 GB host RAM)	64.8 tok/s	5431 ms

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on RTX 3080 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

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 (+4)

Adds memory headroom for longer context windows and future model growth.37.9 tok/s decode

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

~$449 MSRP

RTX 5070 Ti 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.78.3 tok/s decode

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

~$749 MSRP

RTX 4070 Ti Super 16GBNVIDIA upgrade

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.73.4 tok/s decode

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

~$799 MSRP

Frequently asked questions

Can RTX 3080 12GB run gemma 3 12b it?

Yes, RTX 3080 12GB can run gemma 3 12b it with a C grade (Tight fit). Expected decode speed: 94.7 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 11.1 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 3080 12GB?

On RTX 3080 12GB, gemma 3 12b it achieves approximately 94.7 tokens per second decode speed with a time-to-first-token of 2045ms using Q4_K_M quantization.

Can RTX 3080 12GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on RTX 3080 12GB receives a C grade with 94.7 tok/s and 26K context.

What context window can gemma 3 12b it use on RTX 3080 12GB?

On RTX 3080 12GB, gemma 3 12b it can safely use up to 26K 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 3080 12GB?

See all results for RTX 3080 12GB See all hardware for gemma 3 12b it

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